12322670
Discrete cosine transform-based local adaptive filtering of images corrupted by nonstationary noise
In many image-processing applications, observed images are contaminated by a nonstationary noise and no a priori information on noise dependence on local mean or about local properties of noise statistics is available. In order to remove such a noise, a locally adaptive filter has to be applied. We study a locally adaptive filter based on evaluation of image local activity in a "blind" manner and on discrete cosine transform computed in overlapping blocks. Two mechanisms of local adaptation are proposed and applied. The first mechanism takes into account local estimates of noise standard deviation while the second one exploits discrimination of homogeneous and heterogeneous image regions by adaptive threshold setting. The designed filter performance is tested for simulated data as well as for real-life remote-sensing and maritime radar images. Recommendations concerning filter parameter setting are provided. An area of applicability of the proposed filter is defined.
15081895
Image denoising algorithm via doubly local Wiener filtering with directional windows in wavelet domain
Local Wiener filtering in the wavelet domain is an effective image denoising method of low complexity. In this letter, we propose a doubly local Wiener filtering algorithm, where the elliptic directional windows are used for different oriented subbands in order to estimate the signal variances of noisy wavelet coefficients, and the two procedures of local Wiener filtering are performed on the noisy image. The experimental results show that the proposed algorithm improves the denoising performance significantly.
7984390
Signal-dependent noise removal in pointwise shape-adaptive DCT domain with locally adaptive variance
This paper presents a novel effective method for denoising of images corrupted by signal-dependent noise. Denoising is performed by coefficient shrinkage in the shape-adaptive DCT (SA-DCT) transform-domain. The Anisotropic Local Polynomial Approximation (LPA) - Intersection of Confidence Intervals (ICI) technique is used to define the shape of the transform's support in a pointwise adaptive manner. The use of such an adaptive transform support enables both a simpler modelling of the noise in the transform domain and a sparser decomposition of the signal. Consequently, coefficient shrinkage is very effective and the reconstructed estimate's quality is high, in terms of both numerical error-criteria and visual appearance, with sharp detail preservation and clean edges. Simulation experiments demonstrate the superior performance of the proposed algorithm for a wide class of noise models with a signal-dependent variance, including Poissonian (photon-limited imaging), film-grain, and speckle noise.
14473867
Homomorphic wavelet-based statistical despeckling of SAR images
In this paper, we introduce the homomorphic /spl Gamma/-WMAP (wavelet maximum a posteriori) filter, a wavelet-based statistical speckle filter equivalent to the well known /spl Gamma/-MAP filter. We perform a logarithmic transformation in order to make the speckle contribution additive and statistically independent of the radar cross section. Further, we propose to use the normal inverse Gaussian (NIG) distribution as a statistical model for the wavelet coefficients of both the reflectance image and the noise image. We show that the NIG distribution is an excellent statistical model for the wavelet coefficients of synthetic aperture radar images, and we present a method for estimating the parameters. We compare the homomorphic /spl Gamma/-WMAP filter with the /spl Gamma/-MAP filter and and the recently introduced /spl Gamma/-WMAP filter, which are both based on the same statistical assumptions. The homomorphic /spl Gamma/-WMAP filter is shown to have better performance with regard to smoothing homogeneous regions. It may in some cases introduce a small bias, but in our studies it is always less than that introduced by the /spl Gamma/-MAP filter. Further, the speckle removed by the homomorphic /spl Gamma/-WMAP filter has statistics closer to the theoretical model than the speckle contribution removed with the other filters.
6008738
Noise adaptive soft-switching median filter
Existing state-of-the-art switching-based median filters are commonly found to be nonadaptive to noise density variations and prone to misclassifying pixel characteristics at high noise density interference. This reveals the critical need of having a sophisticated switching scheme and an adaptive weighted median filter. We propose a novel switching-based median filter with incorporation of fuzzy-set concept, called the noise adaptive soft-switching median (NASM) filter, to achieve much improved filtering performance in terms of effectiveness in removing impulse noise while preserving signal details and robustness in combating noise density variations. The proposed NASM filter consists of two stages. A soft-switching noise-detection scheme is developed to classify each pixel to be uncorrupted pixel, isolated impulse noise, nonisolated impulse noise or image object's edge pixel. "No filtering" (or identity filter), standard median (SM) filter or our developed fuzzy weighted median (FWM) filter will then be employed according to the respective characteristic type identified. Experimental results show that our NASM filter impressively outperforms other techniques by achieving fairly close performance to that of ideal-switching median filter across a wide range of noise densities, ranging from 10% to 70%
61130398
Nonlinear Signal and Image Processing: Theory, Methods, and Applications
From the Publisher: 
Nonlinear signal and image processing methods are fast emerging as an alternative to established linear methods for meeting the challenges of increasingly sophisticated applications. This book details recent advances in nonlinear theory, techniques, and an array of modern applications. Chapters contributed by well-known theorists and researchers explore current and emerging nonlinear methods from their theoretical background and practical algorithms through the application of nonlinear methods to contemporary problems and the potential of these methods to solve important open problems. Illustrative examples and instructive results are abundantly provided, and supplemental material is available for download from the Internet.
124459454
Color Image Processing and Applications
This book claims to fill a niche in the provision of textbooks devoted to image processing by being devoted to colour aspects. It is aimed at researchers and practitioners working in the area of colour image processing, particularly graduates in Computer Science and Electrical and Computer Engineering. The book is structured in such a way that, after reading an introductory chapter on colour, readers can work their way through the following four chapters on image processing, or omit them and move directly to the final three chapters that deal with colour image analysis and coding. It is not immediately apparent from reading the preface that companion image processing software is available online from the publisher's website, and this is not made use of as an integral part of the book. Regrettably, the book shows much evidence of a lack of rigorous proof reading and editing. A number of errors can be found, particularly in the first chapter; this provides the fundamentals in colour science on which the book is based. For example, on the first page the visible spectrum is incorrectly reproduced with the wrong wavelength scale and photoreceptors are referred to as `roads'. A few pages on there is a complete mismatch between the explanatory text and diagrams concerning the CIE, XYZ and RGB colour matching functions. One diagram appears to have been reproduced twice, is incorrectly titled and not annotated. Also, the CIE chromaticity diagram lacks a wavelength scale and the colours of the diagram are incorrectly reproduced. Unfortunately, these fundamental errors appear in the first ten pages and have the unfortunate effect of detracting from the authoritative nature of the book as a whole. A further example of poor proof reading/editing can be found towards the end of the chapter in which photographic film is defined as follows: `The film which is used by conventional cameras contains three emulsion layers which are sensitive to red and blue light, which enters through the camera lens.' The chapters do improve, however, as one goes through the book, although in chapter 2 the description of the origins of photographic noise is incorrect and incomplete (`the noise is mainly due to the silver grains that precipitate during film exposure'). Also, the origins of noise in photoelectronic sensors are incompletely described. Each chapter is accompanied by a substantial number of references to the primary sources of information, many of which are to recent literature in the field, in very useful summary or conclusion sections. It is puzzling that in view of the significance of the Fourier transform in image processing, it is not included, other than a brief mention of the Discrete Fourier Transform in the chapter on image compression. Adaptive image filters are described at length in chapter 3, which is followed by chapters dedicated to colour edge detection, enhancement and restoration, image segmentation, image compression and emerging applications. The latter makes interesting reading but is based almost exclusively on the detection and automatic location of the human face. The index is not very exhaustive and as a consequence it is difficult to find many items that are discussed in the text but are not indexed. A few examples include: Wiener filter, Sobel, Prewitt and Robert's edge detection, all of which appear in the text and in the indices of most books on image processing but not in the index to this book. Also, most of the existing texts on image processing include many aspects of colour, which detracts somewhat from this book's claimed uniqueness, although it does contain more state-of-the-art aspects. Ralph Jacobson
128685787
Filtering of radar images based on blind evaluation of noise characteristics
A common assumption concerning noise in radar images is that it is of multiplicative nature and spatially uncorrelated. Meanwhile, recent studies have shown that additive noise component cannot be neglected, especially for images formed by side look aperture radars (SLARs). Moreover, majority of radar image filtering techniques are designed under assumption that noise is i.i.d., i.e. spatially uncorrelated. However, in many practical situations the latter assumption is not true. Besides, spatial correlation properties of noise can be different and they are often a priori unknown. In this paper we demonstrate that complex statistical and spatial correlation characteristics of noise in radar images can and should be taken into consideration at image filtering stage. We design a modification of the denoising algorithm based on discrete cosine transform (DCT) that is able to easily incorporate a priori information or obtained estimates of noise statistical and spatial correlation characteristics. This can be done in automatic (blind) manner due to utilizing a sequence of blind estimation operations. We present simulation results that show appropriate accuracy and robustness of these operations. Finally, real life image filtering examples are given that confirm the effectiveness of the designed techniques.
118652414
Fundamentals of Nonlinear Digital Filtering
Nonlinear Signal Processing Signal Processing Model Signal and Noise Models Fundamental Problems in Noise Removal Algorithms Statistical Preliminaries Random Variables and Distributions Signal and Noise Models Estimation Some Useful Distributions 1001 Solutions Trimmed Mean Filters Other Trimmed Mean Filters L-Filters C-Filters (Ll-Filters) Weighted Median Filters Ranked-Order and Weighted Order Statistic Filters Multistage Median Filters Median Hybrid Filters Edge-Enhancing Selective Filters Rank Selection Filters M-Filters R-Filters Weighted Majority with Minimum Range Filters Nonlinear Mean Filters Stack Filters Generalizations of Stack Filters Morphological Filters Soft Morphological Filters Polynomial Filters Data-Dependent Filters Decision-Based Filters Iterative, Cascaded, and Recursive Filters Some Numerical Measures of Nonlinear Filters Discussion Statistical Analysis and Optimization of Nonlinear Filters Methods Based on Order Statistics Stack Filters Multistage and Hybrid Filters Discussion Exercises Bibliography Index
10226158
A review of speckle filtering in the context of estimation theory
Speckle filter performance depends strongly on the speckle and scene models used as the basis for filter development. These models implicitly incorporate certain assumptions about speckle, scene, and observed signals. In this study, the multiplicative and the product speckle models, which have been used for the development of most of the well-known filters, are analyzed, and their implicit assumptions with regard to the stationarity-nonstationarity nature of speckle are discussed. This leads to the definition of two categories of speckle filters: the stationary and the nonstationary multiplicative speckle model filters. The various approximate models used for the multiplicative speckle noise model are assessed as functions of speckle and scene characteristics to derive the requirements on scene signal variations for the validity of both the stationary and nonstationary multiplicative speckle models. Speckle filtering is then studied in the context of estimation theory, so as to develop a procedure for speckle filtering. It is shown that speckle filtering can be effective only in locally stationary scenes. Regions in which the signals are not stationary have to be filtered separately using a priori scene templates for the best matching of nonstationary scene features. The use of multiresolution techniques is crucial for accurate estimation of filter parameters. Under the guidance of the speckle filtering procedure, structural-multiresolution versions of the Lee (1980) and Frost et al. (1982) filters are developed for optimum application of these filters in the context of nonstationary scene signals.
28284187
Understanding Synthetic Aperture Radar Images
Introduction. Principles of SAR Image Formation. Image Defects and their Correction. Fundamental Properties of SAR Images. Data Models. RCS Reconstruction Filters. RCS Classification and Segmentation. Texture Exploitation. Correlated Textures. Information in Multi-Channel SAR. Analysis Techniques for Multi-Dimensional Images. Target Information. Image Classification.
20991028
Improved Sigma Filter for Speckle Filtering of SAR Imagery
The Lee sigma filter was developed in 1983 based on the simple concept of two-sigma probability, and it was reasonably effective in speckle filtering. However, deficiencies were discovered in producing biased estimation and in blurring and depressing strong reflected targets. The advancement of synthetic aperture radar (SAR) technology with high-resolution data of large dimensions demands better and efficient speckle filtering algorithms. In this paper, we extend and improve the Lee sigma filter by eliminating these deficiencies. The bias problem is solved by redefining the sigma range based on the speckle probability density functions. To mitigate the problems of blurring and depressing strong reflective scatterers, a target signature preservation technique is developed. In addition, we incorporate the minimum-mean-square-error estimator for adaptive speckle reduction. Simulated SAR data are used to quantitatively evaluate the characteristics of this improved sigma filter and to validate its effectiveness. The proposed algorithm is applied to spaceborne and airborne SAR data to demonstrate its overall speckle filtering characteristics as compared with other algorithms. This improved sigma filter remains simple in concept and is computationally efficient but without the deficiencies of the original Lee sigma filter.
7927563
CHRIS-PROBA PERFORMANCE EVALUATION : SIGNAL-TO-NOISE RATIO , INSTRUMENT EFFICIENCY AND DATA QUALITY FROM ACQUISITIONS OVER SAN ROSSORE ( ITALY ) TEST SITE
A methodology for evaluating CHRIS performance is presented. Starting from hyperspectral images acquired over San Rossore (Italy) test site, different issues concerning instrument response are investigated. Particular attention is devoted to the assessment of the noise (due to fully stochastic process) affecting the images and to the estimation of signal-to-noise ratio by means of an innovative algorithm developed by the authors. This procedure analyses the bit-planes extracted from any monochromatic image in the hyperspectral data cube, then it assesses the randomness of every bit-plane, and finally computes the signal-to-noise ratio for each spectral channel. Differently from more traditional signalto-noise estimators, which need to select an homogeneous area in the concerned image to isolate noise contribution only, our algorithm is insensitive to scene texture. This circumstance allows the developed method to completely process the image of any observed ground area. The paper discusses other possible sources of systematic disturbance (like stripe-noise, smear effect, and so forth) in order to assess the overall data quality. Finally, comparisons between estimated and expected instrument efficiency are presented and discussed.
2418365
Removing Impulse Bursts from Images by Training-Based Filtering
The characteristics of impulse bursts in remote sensing images are analyzed and a model for this noise is proposed. The model also takes into consideration other noise types, for example, the multiplicative noise present in radar images. As a case study, soft morphological filters utilizing a training-based optimization scheme are used for the noise removal. Different approaches for the training are discussed. It is shown that these techniques can provide an effective removal of impulse bursts. At the same time, other noise types in images, for example, the multiplicative noise, can be suppressed without compromising good edge and detail preservation. Numerical simulation results, as well as examples of real remote sensing images, are presented.
32186316
Center weighted median filters: Some properties and their applications in image processing
Abstract Several deterministic properties of center weighted median (CWM) filters are analyzed in this paper. The root structures of CWM filters are derived. A test is devised to check whether a signal is a root of a given CWM filter. The convergence behaviour of recursive and nonrecursive CWM filters is then analyzed. In particular, based on their root structures, it is proven that repeated filtering on any appended finite length signal by any CWM filter produces roots in a finite number of filter passes. A synthesis method of CWM filters under structural constraints is presented. A separable CWM filter is proposed as an application of one-dimensional CWM filters in image processing. It is expected that by using CWM filters, more details can be preserved along the horizontal and vertical directions. Adaptive separable CWM filters are then proposed. We show that they can be better than two-dimensional adaptive CWM filters with the same window size in some cases. Adaptive CWM filters are then extended to adaptive symmetric weighted median filters.
14502908
Scale-Space and Edge Detection Using Anisotropic Diffusion
A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >
119616827
ESTIMATION OF A FUNCTION WITH DISCONTINUITIES VIA LOCAL POLYNOMIAL FIT WITH AN ADAPTIVE WINDOW CHOICE
We propose a method of adaptive estimation of a regression function and which is near optimal in the classical sense of the mean integrated error. At the same time, the estimator is shown to be very sensitive to discontinuities or change-points of the underlying function f or its derivatives. For instance, in the case of a jump of a regression function, beyond the interval of length (in order) n-1 log n around change-points the quality of estimation is essentially the same as if locations of jumps were known. The method is fully adaptive and no assumptions are imposed on the design, number and size of jumps. The results are formulated in a non-asymptotic way and can be therefore applied for an arbitrary sample size.
122523543
A wavelet tour of signal processing
Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.
17594958
Survey of Image Denoising Techniques
Removing noise from the original signal is still a challenging problem for researchers. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. This paper presents a review of some significant work in the area of image denoising. After a brief introduction, some popular approaches are classified into different groups and an overview of various algorithms and analysis is provided. Insights and potential future trends in the area of denoising are also discussed.
10896413
Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal
This work addresses the problem of signal-dependent noise removal in images. An adaptive nonlinear filtering approach in the orthogonal transform domain is proposed and analyzed for several typical noise environments in the DCT domain. Being applied locally, that is, within a window of small support, DCT is expected to approximate the Karhunen-Loeve decorrelating transform, which enables effective suppression of noise components. The detail preservation ability of the filter allowing not to destroy any useful content in images is especially emphasized and considered. A local adaptive DCT filtering for the two cases, when signal-dependent noise can be and cannot be mapped into additive uncorrelated noise with homomorphic transform, is formulated. Although the main issue is signal-dependent and pure multiplicative noise, the proposed filtering approach is also found to be competing with the state-of-the-art methods on pure additive noise corrupted images.
17209176
An integro-differential method for adaptive filtering of additive or multiplicative noise
We present a new adaptive filtering method for either additive noise or multiplicative noise. The proposed method is stated with a differential equation of the temporal evolution of the problem of interest. It achieves an improvement in the efficiency of the well-known iterated Lee filter in the spatial domain. Mainly, it incorporates a local determination of the optimal regions which are subsequently used to estimate the different local statistics involved in the filtering method. This estimation is carried out differently according to the nature of the processed pixel. An adapted decisional criterion indicates if the pixel belongs either to a contour or to a homogeneous zone, following the idea used in classical anisotropic methods. Then, the efficiency of the proposed method, for which we can prove the existence and uniqueness of the solution, is assessed on several images degraded artificially. The results are compared to the main used filters in order to confirm the theoretical findings.
11552863
Signal-dependent noise removal in the undecimated wavelet domain
In this paper, methods to denoise images corrupted by a signal-dependent additive distortion are proposed. The noise model is parametric to take into account different noise generation processes. Noise reduction is approached as a Wiener-like filtering performed in a shift-invariant wavelet domain by means of an adaptive rescaling of the coefficients of an undecimated decomposition. The scaling factor is computed by using the statistics estimated from the degraded image and the parameters of the noise model. The absence of decimation in the wavelet decomposition avoids the ringing impairments produced by critically-subsampled wavelet-based denoising. Experimental results demonstrate that excellent background smoothing as well as preservation of edge sharpness and texture can be obtained.
1920558
Bivariate shrinkage with local variance estimation
The performance of image-denoising algorithms using wavelet transforms can be improved significantly by taking into account the statistical dependencies among wavelet coefficients as demonstrated by several algorithms presented in the literature. In two earlier papers by the authors, a simple bivariate shrinkage rule is described using a coefficient and its parent. The performance can also be improved using simple models by estimating model parameters in a local neighborhood. This letter presents a locally adaptive denoising algorithm using the bivariate shrinkage function. The algorithm is illustrated using both the orthogonal and dual tree complex wavelet transforms. Some comparisons with the best available results are given in order to illustrate the effectiveness of the proposed algorithm.
30854164
Data Compression: The Complete Reference
Data compression is one of the most important fields and tools in modern computing. From archiving data, to CD ROMs, and from coding theory to image analysis, many facets of modern computing rely upon data compression. Data Compression provides a comprehensive reference for the many different types and methods of compression. Included are a detailed and helpful taxonomy, analysis of most common methods, and discussions on the use and comparative benefits of methods and description of "how to" use them. The presentation is organized into the main branches of the field of data compression: run length encoding, statistical methods, dictionary-based methods, image compression, audio compression, and video compression. Detailed descriptions and explanations of the most well-known and frequently used compression methods are covered in a self-contained fashion, with an accessible style and technical level for specialists and nonspecialists. Topics and features: coverage of video compression, including MPEG-1 and H.261 thorough coverage of wavelets methods, including CWT, DWT, EZW and the new Lifting Scheme technique complete audio compression QM coder used in JPEG and JBIG, including new JPEG 200 standard image transformations and detailed coverage of discrete cosine transform and Haar transform coverage of EIDAC method for compressing simple images prefix image compression ACB and FHM curve compression geometric compression and edgebreaker technique.Data Compression provides an invaluable reference and guide for all computer scientists, computer engineers, electrical engineers, signal/image processing engineers and other scientists needing a comprehensive compilation for a broad range of compression methods.
63140746
Digital Holography And Digital Image Processing Principles Methods Algorithms
Thank you very much for reading digital holography and digital image processing principles methods algorithms. As you may know, people have search hundreds times for their favorite novels like this digital holography and digital image processing principles methods algorithms, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful bugs inside their laptop.
36475123
A method for realizing reversible integer type-IV discrete cosine transform (IntDCT-IV)
Integer transform plays important role in signal lossless compression. In order to achieve high accuracy to its corresponding real value transform, the rounding number should be as low as possible. In this paper, we propose a new method for realizing the reversible integer discrete cosine transform type IV (DCT-IV), The proposed method exhibits low rounding number (1.5 N), low complexity O(Nlog/sub 2/N) and is the most accurate to its counterpart floating point DCT-IV transformation when the processing block length is higher than 16.
2609828
Adaptive DCT-based filtering of images corrupted by spatially correlated noise
Majority of image filtering techniques are designed under assumption that noise is of special, a priori known type and it is i.i.d., i.e. spatially uncorrelated. However, in many practical situations the latter assumption is not true due to several reasons. Moreover, spatial correlation properties of noise might be rather different and a priori unknown. Then the assumption that noise is i.i.d. under real conditions of spatially correlated noise commonly leads to considerable decrease of a used filter effectiveness in comparison to a case if this spatial correlation is taken into account. Our paper deals with two basic aspects. The first one is how to modify a denoising algorithm, in particular, a discrete cosine transform (DCT) based filter in order to incorporate a priori or preliminarily obtained knowledge of spatial correlation characteristics of noise. The second aspect is how to estimate spatial correlation characteristics of noise for a given image with appropriate accuracy and robustness under condition that there is some a priori information about, at least, noise type and statistics like variance (for additive noise case) or relative variance (for multiplicative noise). We also present simulation results showing the effectiveness (the benefit) of taking into consideration noise correlation properties.
17433902
ADAPTIVE DCT-BASED 1-D FILTERING OF POISSON AND MIXED POISSON AND IMPULSIVE NOISE
Practical cases where a 1-D signal is corrupted by mixed Poisson and specific impulsive noise are considered. The requirements to the filtering method are discussed. It is shown that DCT based filters combined in adaptive manner with some robust filter, e.g., a standard median filter, can effectively be applied for noise removal in the considered case. Different modifications of the DCT based filter are analyzed. It is demonstrated that the DCT based filter can either be applied together with a pair of Anscombe pointwise transforms or in a “signal-dependent” manner without such transforms. It is shown via numerical simulations that the latter way may be preferable.
128896377
Improved minimal inter-quantile distance method for blind estimation of noise variance in images
Multichannel (multi and hyperspectral, dual and multipolarization, multitemporal) remote sensing (RS) is widely used in different applications. Noise is one of the basic factors that deteriorates RS data quality and prevents retrieval of useful information. Because of this, image pre-filtering is a typical stage of multichannel RS data pre-processing. Most efficient modern filters and other image processing techniques employ a priori information on noise type and its statistical characteristics like variance. Thus, there is an obvious need in automatic (blind) techniques for determination of noise type and its characteristics. Although several such techniques have been already developed, not all of them are able to perform appropriately in cases when considered images contain a large percentage of texture regions and other locally active areas. Recently we have designed a method of blind determination of noise variance based on minimal inter-quantile distance. However, it occurred that its accuracy could be further improved. In this paper we describe and analyze several ways to do this. One opportunity deals with better approximation of inter-quantile distance curve. Another opportunity concerns the use of image pre-segmentation before forming an initial set of local estimates of noise variance. Both ways are studied for model data and test images. Numerical simulation results confirm improvement of estimate accuracy for the proposed approach.
62686116
Segmentation-based method for blind evaluation of noise variance in images
Noise is one of the basic factors that degrade remote sensing (RS) data and prevent accurate and reliable retrieval of useful information. Availability of a priori information on noise type and properties allows applying more effective methods for image processing, namely, filtering, edge detection, feature extraction, etc. However, noise statistics are often unknown and are to be estimated for an image at hand. Thus one needs blind methods for the evaluation of the noise variance, especially if the number of images or sub-band images of multichannel RS data is large enough. In this paper, we consider several approaches to blind evaluation of noise variance. An important item is that we consider both i.i.d. and spatially correlated noise. It is demonstrated that some techniques that perform well enough for i.i.d. noise fail if the image is corrupted by spatially correlated noise. We show how segmentation-based methods for blind evaluation of noise variance that operate in the spatial domain can be modified in order to provide better accuracy for wide ranges of noise variance and spatial correlation parameters. Numerical simulation results comparing the performance of several techniques are presented. Real RS data processing examples are also given.
5971306
Blind evaluation of additive noise variance in textured images by nonlinear processing of block DCT coefficients
The problem of blind evaluation of noise variance in images is considered. Typical approaches commonly presume getting a set of variance estimations in small size blocks and further analysis of the obtained estimations set distribution with finding its maximum. However, such methods suffer from the common drawback that their accuracy becomes drastically worse if an image contains a lot of texture. To alleviate this drawback we propose an approach based on the fact that the statistical properties of DCT coefficients corresponding to high spatial frequencies in small size blocks greatly depend upon noise variance. As shown, these coefficients can be processed in nonlinear manner in order to eliminate the influence of informative component of the image itself. The dependence of the method accuracy on the used nonlinear operation and its parameters is carried out. It is shown that the proposed method produces appropriately good accuracy of blind evaluation of noise variance for a set of considered test images. The comparison analysis of the proposed method and some known analogs is performed.
2932038
Automatic Estimation and Removal of Noise from a Single Image
Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches cannot effectively remove color noise produced by today's CCD digital camera. In this paper, we propose a unified framework for two tasks: automatic estimation and removal of color noise from a single image using piecewise smooth image models. We introduce the noise level function (NLF), which is a continuous function describing the noise level as a function of image brightness. We then estimate an upper bound of the real NLF by fitting a lower envelope to the standard deviations of per-segment image variances. For denoising, the chrominance of color noise is significantly removed by projecting pixel values onto a line fit to the RGB values in each segment. Then, a Gaussian conditional random field (GCRF) is constructed to obtain the underlying clean image from the noisy input. Extensive experiments are conducted to test the proposed algorithm, which is shown to outperform state-of-the-art denoising algorithms.
46385009
Local adaptive filtering of images corrupted by nonstationary noise
In various practical situations of remote sensing image processing it is assumed that noise is nonstationary and no a priory information on noise dependence on local mean or about local properties of noise statistics is available. It is shown that in such situations it is difficult to find a proper filter for effective image processing, i.e., for noise removal with simultaneous edge/detail preservation. To deal with such images, a local adaptive filter based on discrete cosine transform in overlapping blocks is proposed. A threshold is set locally based on a noise standard deviation estimate obtained for each block. Several other operations to improve performance of the locally adaptive filter are proposed and studied. The designed filter effectiveness is demonstrated for simulated data as well as for real life radar remote sensing and marine polarimetric radar images.
6232278
Image Denoising using Gaussian Scale Mixtures in the Wavelet Domain
We describe a method for removing noise from digital images, based on a statistical model of the coecien ts of an overcomplete multi-scale oriented basis. Neighborhoods of coecien ts at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vector and a hidden positive scalar multiplier. The latter modulates the local variance of the coecien ts in the neighborhood, and is thus able to account for the empirically observed correlation between the amplitudes of pyramid coecien ts. Under this model, the Bayesian least squares estimate of each coecien t reduces to a weighted average of the local linear (Wiener) estimate over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive Gaussian noise of known covariance that the performance of this method substantially surpasses that of previously published methods, both visually and in terms of mean squared error. In addition, we demonstrate the performance of the algorithm in removing sensor noise from high-ISO digital camera images.
24473896
Edge detection and filtering of images corrupted by nonstationary noise using robust statistics
Images are a type of data widely used, processed and analyzed in CAD and telecommunication systems. To retrieve useful information from images, they are often subject to different kinds of preprocessing that commonly include edge detection and filtering. These operations can be performed by standard means if noise type and statistics are known in advance. In this paper we address situations when such a priori information is not available. Two local operators based on robust statistics calculated in spatial and spectral domains are proposed and analyzed for edge detection application. Then we show how the obtained edge maps can be exploited in locally adaptive filtering based on discrete cosine transform (DCT).
6315518
Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation
Abstract
A novel adaptive and exemplar-based approach is proposed for image restoration (denoising) and representation. The method is based on a pointwise selection of similar image patches of fixed size in the variable neighborhood of each pixel. The main idea is to associate with each pixel the weighted sum of data points within an adaptive neighborhood. We use small image patches (e.g. 7×7 or 9×9 patches) to compute these weights since they are able to capture local geometric patterns and texels seen in images. In this paper, we mainly focus on the problem of adaptive neighborhood selection in a manner that balances the accuracy of approximation and the stochastic error, at each spatial position. The proposed pointwise estimator is then iterative and automatically adapts to the degree of underlying smoothness with minimal a priori assumptions on the function to be recovered. The method is applied to artificially corrupted real images and the performance is very close, and in some cases even surpasses, to that of the already published denoising methods. The proposed algorithm is demonstrated on real images corrupted by non-Gaussian noise and is used for applications in bio-imaging.

27416559
Preliminary Automatic Analysis of Characteristics of Hypespectral Aviris Images
The need to perform analysis and processing hyperspectral images registered by AVIRIS imager is stated. AVIRIS system basic characteristics are briefly discussed. Then, novel approaches and robust methods for estimation of noise variance and signal-to-noise ratios in band images are proposed. Using them, real life sets of AVIRIS images are analyzed. The basic dependences and conclusions following from the carried out study are presented and considered. Some examples showing that some band images are worth denoising are presented. The methods well suited for this purpose are recommended. Possible approaches to hyperspectral data compression are also discussed
13983818
Quality criteria benchmark for hyperspectral imagery
Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Efficient compression techniques are required, and lossy compression, specifically, will have a role to play, provided its impact on remote sensing applications remains insignificant. To assess the data quality, suitable distortion measures relevant to end-user applications are required. Quality criteria are also of a major interest for the conception and development of new sensors to define their requirements and specifications. This paper proposes a method to evaluate quality criteria in the context of hyperspectral images. The purpose is to provide quality criteria relevant to the impact of degradations on several classification applications. Different quality criteria are considered. Some are traditionally used in image and video coding and are adapted here to hyperspectral images. Others are specific to hyperspectral data. We also propose the adaptation of two advanced criteria in the presence of different simulated degradations on AVIRIS hyperspectral images. Finally, five criteria are selected to give an accurate representation of the nature and the level of the degradation affecting hyperspectral data.