Grayscale transformation  Image enhancement
216652178
Image Enhancement Algorithm Based on Wavelet Transform
In recent years, with the rapid development of science and technology, as a mathematical analysis tool, wavelet transform with multi scale resolution features has replaced the role of the traditional technical methods in many engineering applications. This thesis first introduces the basic theory of wavelet transformation and the basic principle of the related image in de noising and enhancement. Second, the thesis is combined with the characteristics of the seismic image and the noise, and the seismic image de noising and enhancement are discussed in detail by wavelet analysis. The soft and hard threshold methods have been selected. And the threshold function of the wavelet has been bettered; then the histogram equalization is used to improve the image contrast. Finally, it is concluded that the new threshold function can effectively remove the noise and obviously enhance the effect of the image de noising.
Wenjun Zhang Chao Chen
225118806
Low contrast image enhancement using spatial contextual similarity histogram computation and color reconstruction
Abstract This paper presents an adaptive enhancement framework for low contrast images to improve the deteriorated details and color information. The proposed algorithm is composed of two adaptive steps: Contrast enhancement and Color restoration. Firstly, according to the luminance map separated from the RGB image, a new spatial contextual similarity histogram (SCSH) is developed. The new SCSH considers the gray level similarities among the adjacent pixels, following the standard deviation value of the luminance image. The contrast enhanced image is obtained by applying the intensity transformation function derived from the cumulative distribution function obtained from the normalized values of SCSH. An adaptive weighted fusion is carried out between the luminance enhanced channel and original luminance channel with the weights computed from the enhanced luminance channel. Secondly, an adaptive color restoration technique is designed to reconstruct the more consistent color image using original chromatic information and modified luminance. The results are analyzed using the statistical paired t test. Both objective and subjective experimentation prove the supremacy of the proposed algorithm with conventional and state of the art algorithms.
J. Frankl. Inst.
Kankanala Srinivas Ashish Kumar Bhandari Ashutosh Kumar Singh
164835939
Adaptive image enhancement method for correcting low illumination images
Abstract In this study, to improve the adaptability of image enhancement in images with low illumination, a colored image correction method based on nonlinear functional transformation according to the illumination reflection model and multiscale theory is proposed. First, the original RGB image is converted to HSV color space, and the V component is used to extract the illumination component of the scene using the multiscale Gaussian function. Then, a correction function is constructed based on the Weber Fechner law, and two images are obtained through adaptive adjustments to the image enhancement function parameters based on the distribution profiles of the illumination components. Finally, an image fusion strategy is formulated and used to extract the details from the two images. Compared with the classic algorithm, the proposed algorithm can improve the overall brightness and contrast of an image while reducing the impact of uneven illumination. The enhanced images appear clear, bright, and natural.
Inf. Sci.
Wencheng Wang Zhenxue Chen Xiaohui Yuan Xiaojin Wu
219616278
Optimization of Feature Loss for Image Enhancement
Image transformation problem is a problem in which an input image is transformed to an output image. In most of the recent methods, a feed forward neural network is defined which utilizes per pixel loss between the output image and the ground truth image. In this paper we have showcased that highquality images can be generated by defining a feature loss function which is based on high level perceptual features extracted from pre trained convolutional networks. We have combined both the approaches that have been formerly mentioned and have proposed a feature loss function for training a feed forward neural network capable of image transformation tasks. We have compared out method with that of an optimization based approach, similar to the one utilized in Generative Adversarial Networks (GANs) and our method produced visually appealing results whilst fully capturing the intricate details of the object in the image.
Vishal Burman Harshinee Sriram S Usha Kiruthika
85500760
Remote Sensing Image Enhancement Via Edge Preserving Multiscale Retinex
Recently, an image enhancement receives a considerable research attention due to its wide application. In this paper, we propose a novel algorithm to enhance the contrast of remote sensing image while avoiding color distortion and detail loss. First, based on the Sobel operator strength, the local adaptive weighting parameter is obtained to penalize the fixed regularization parameter of original guided filtering. Then, the grayscale difference between flat patch and edge patch, which is caused by the fixed regularization parameter is amplified meanwhile without sacrificing the denoising performance. Finally, the Gaussian filtering of original multiscale retinex with color restoration is replaced with the guided filtering based on the Sobel edge detection operator to estimate the incident component of an image. Comparing with the previous algorithms, the proposed one can obtain more edge details and better color fidelity but with lower computational complexity and higher efficiency.
IEEE Photonics Journal
Lu Teng Feng Xue Qiudi Bai
212575547
Spatial domain image enhancement techniques for acute myeloid leukemia (M1,M4,M5,M7)
In this paper, several techniques of image enhancement spatial domain is elucidated and analyzed for the purpose of enhancing Acute Myeloid Leukemia (AML) subtype of M1, M4, M5 and M7. The techniques involved contrast stretching of greyscale images, image subtraction and image sharpening. The three methods compared with one another to achieve the highest PSNR value for the suitability technique of AML subtypes (M1, M4, M5 and M7) Firstly, subtypes images converted into grayscale. Then, each four images tested with contrast stretching techniques followed by image subtraction and image sharpening. The performances were evaluated based on Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) Due to its higher value obtained, image sharpening is a good enhancement techniques for Acute Myeloid Leukemia with 68.2083 dB and the lowest MSE achieved of 0.0103.
Indonesian Journal of Electrical Engineering and Computer Science
A S A Salam M N M Isa M I Ahmad
57256842
Image Enhancement Using Fuzzy Logic Techniques
Image enhancement is the preprocessing task in digital image processing. It helps to improve the appearance or perception of the image so that the image can be used for analytics and human visual system. Image enhancement techniques lie in three broad categories spatial domain, frequency domain, and fuzzy domain based enhancement. A lot of work has been done on image enhancement. Most of the work has been done/performed on grayscale image. This paper concentrates on image enhancement using fuzzy logic approach and gives an insight into previous research work and future perspectives.
Preeti Mittal Rajesh Kumar Saini Neeraj Kumar Jain
61807960
A Pipeline Neural Network for Low Light Image Enhancement
Low light image enhancement is an important challenge in computer vision. Most of the low light images taken in low light conditions usually look noisy and dark, which makes it more difficult for subsequent computer vision tasks. In this paper, inspired by multi scale retinex, we present a low light image enhancement pipeline network based on an end to end fully convolutional networks and discrete wavelet transformation (DWT) First, we show that multiscale retinex (MSR) can be considered as a convolutional neural network with Gaussian convolution kernel, and blending the result of DWT can improve the image produced by MSR. Second, we propose our pipeline neural network, consisting of denoising net and low light image enhancement net, which learns a function from a pair of dark and bright images. Finally, we evaluate our method both in the synthetic dataset and public dataset. The experiments reveal that in comparison with other state of the art methods, our methods achieve a better performance in the perspective of qualitative and quantitative analyses.
IEEE Access
Yanhui Guo Xue Ke Jie Ma Jun Zhang
149589363
A Review on Image Enhancement Techniques
Image Enhancement is one of the most important and complex techniques in image processing technology. The main aim of image enhancement is to improve the visual appearance on an image and to offer a better representation of the image for Computer Vision Algorithms. In this paper,we is covered a few application fields of image enhancement with various images like grayscale, color, infrared and even with videos. The main objective of this paper is to highlight the drawbacks of the state of the art image enhancement techniques.
Southeast Europe Journal of Soft Computing
Haris Ackar Ali Abd Almisreb Mohamed Ali Saleh
201874109
An inquiry on image enhancement methods of remote sensing images
Remote sensing image enhancement algorithm, which can enhance the contrast of image without changing its information, is crucial for the acquisition and analysis of remote sensing information. In this paper, we compare the performance of several state of the art enhancement algorithms in processing remote sensing images. We classify and refine the main idea of each algorithm, and evaluate the enhancement effect with subjective evaluation and objective metrics such as entropy, brightness, contrast and mean gradient. In order to avoid color enhanced interference, this paper took experiments both on RGB pictures and grayscale pictures. The significance and compared results were shown in the following paper.
International Conference on Digital Image Processing
Jiwei Liu Xiaoqiu Pu Desheng Wen Zongxi Song Junyu Chen