photo
211227
Photo Realistic Single Image Super Resolution Using a Generative Adversarial Network
Despite the breakthroughs in accuracy and speed of single image super resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super resolve at large upscaling factors? The behavior of optimization based super resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal to noise ratios, but they are often lacking high frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super resolution (SR) To our knowledge, it is the first framework capable of inferring photo realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super resolved images and original photo realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo realistic textures from heavily downsampled images on public benchmarks. An extensive mean opinion score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high resolution images than to those obtained with any state of the art method.
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Christian Ledig Lucas Theis Ferenc Huszar Jose Caballero Andrew P Aitken Alykhan Tejani Johannes Totz Zehan Wang Wenzhe Shi
1277217
StackGAN: Text to Photo Realistic Image Synthesis with Stacked Generative Adversarial Networks
Synthesizing high quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256.256 photo realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub problems through a sketch refinement process. The Stage I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage I low resolution images. The Stage II GAN takes Stage I results and text descriptions as inputs, and generates high resolution images with photo realistic details. It is able to rectify defects in Stage I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state of the arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo realistic images conditioned on text descriptions.
2017 IEEE International Conference on Computer Vision (ICCV)
Han Zhang Tao Xu Hongsheng Li
13385757
Photo tourism: exploring photo collections in 3D
We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.
SIGGRAPH 2006
Noah Snavely Steven M Seitz Richard Szeliski
105212778
A novel a Fe2O3@g C3N4 catalyst: Synthesis derived from Fe based MOF and its superior photo Fenton performance
Abstract Fabricating heterojunction catalysts is a promising strategy for improving the performance in heterogeneous photo Fenton reaction (PFR) Herein, a Z scheme heterostructured a Fe2O3@g C3N4 catalyst was successfully synthesized through the co calcination of melamine and Fe based MOF. The characterization results demonstrated that a Fe2O3 nanoparticles anchored on the surface of g C3N4 successfully. The degradation of tetracycline (TC) in visible light/H2O2 system was adopted to evaluate the photo Fenton activity of the catalysts. About 92% of TC was degraded by the optimum composite FOCN 0.45 in 60 min; the degradation rate (0.042 min 1) of TC by the FOCN 0.45 is 6, 7 and 14 times higher than that by pristine MIL 53 (Fe) (0.007 min 1) a Fe2O3 (0.006 min 1) and g C3N4 (0.003 min 1) respectively. The prepared FOCN 0.45 composite exhibited excellent performance and high stability in a wide range of pH value. The promoted photo Fenton catalytic efficiency benefited from the Z scheme heterojunctions of a Fe2O3@g C3N4, which enhanced the separation ability of photo generated charge carriers and increased the electrons that participated in Fe2+/Fe3+ cycle. The boosting OH radicals degraded organic pollutants as main reactive radicals. This work presents a feasible path to design and synthesize heterogeneous photo Fenton catalysts for the removal of organic pollutants.
Applied Surface Science
Ting Guo Kai Wang Gaoke Zhang Xiaoyong Wu
103985331
Rational design of electrocatalysts and photo(electro)catalysts for nitrogen reduction to ammonia (NH3) under ambient conditions
As one of the most important chemicals and carbon free energy carriers, ammonia (NH3) has a worldwide annual production of ~150 million tons, and is mainly produced by the traditional high temperature and high pressure Haber Bosch process which consumes massive amounts of energy. Very recently, electrocatalytic and photo(electro)catalytic reduction of N2 to NH3, which can be performed at ambient conditions using renewable energy, have received tremendous attention. The overall performance of these electrocatalytic and photo(electro)catalytic systems is largely dictated by their core components, catalysts. This perspective for the first time highlights the rational design of electrocatalysts and photo(electro)catalysts for N2 reduction to NH3 under ambient conditions. Fundamental theory of catalytic reaction pathways for the N2 reduction reaction and the corresponding material design principles are introduced first. Then, recently developed electrocatalysts and photo(electro)catalysts are summarized, with a special emphasis on the relationship between their physicochemical properties and NH3 production performance. Finally, the opportunities in this emerging research field, in particular, the strategy of combining experimental and theoretical techniques to design efficient and stable catalysts for NH3 production, are outlined.
Chun Xian Guo Jingrun Ran Anthony Vasileff Shizhang Qiao
206596668
Deep Photo Style Transfer
This paper introduces a deep learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. Our contribution is to constrain the transformation from the input to the output to be locally affine in colorspace, and to express this constraint as a custom fully differentiable energy term. We show that this approach successfully suppresses distortion and yields satisfying photorealistic style transfers in a broad variety of scenarios, including transfer of the time of day, weather, season, and artistic edits.
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Fujun Luan Sylvain Paris Eli Shechtman Kavita Bala
14041600
Talking about pictures: A case for photo elicitation
This paper is a definition of photo elicitation and a history of its development in anthropology and sociology. The view of photo elicitation in these disciplines, where the greatest number of photo elicitation studies have taken place, organizes photo elicitation studies by topic and by form. The paper also presents practical considerations from a frequent photo elicitation researcher and concludes that photo elicitation enlarges the possibilities of conventional empirical research. In addition, the paper argues that photo elicitation also produces a different kind of information. Photo elicitation evokes information, feelings, and memories that are due to the photograph's particular form of representation.
Douglas A Harper
13890001
Neural Photo Editing with Introspective Adversarial Networks
The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images. To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GAN. Our model efficiently captures long range dependencies through use of a computational block based on weight shared dilated convolutions, and improves generalization performance with Orthogonal Regularization, a novel weight regularization method. We validate our contributions on CelebA, SVHN, and CIFAR 100, and produce samples and reconstructions with high visual fidelity.
ICLR
Andrew Brock Theodore Lim James M Ritchie Nick Weston
22896247
Macroscopic Polarization Enhancement Promoting Photo and Piezoelectric Induced Charge Separation and Molecular Oxygen Activation.
Efficient photo and piezoelectric induced molecular oxygen activation are both achieved by macroscopic polarization enhancement on a noncentrosymmetric piezoelectric semiconductor BiOIO3 The replacement of V5+ ions for I5+ in IO3 polyhedra gives rise to strengthened macroscopic polarization of BiOIO3 which facilitates the charge separation in the photocatalytic and piezoelectric catalytic process, and renders largely promoted photo and piezoelectric induced reactive oxygen species (ROS) evolution, such as superoxide radicals O2 and hydroxyl radicals OH) This work advances piezoelectricity as a new route to efficient ROS generation, and also discloses macroscopic polarization engineering on improvement of multi responsive catalysis.
Angewandte Chemie
Hong-wei Huang Shuchen Tu Chao Zeng Tierui Zhang Ali H Reshak Yihe Zhang
100579676
Homogeneous photo Fenton processes at near neutral pH: A review
Abstract The occurrence of new emerging contaminants in surface waters has recently grabbed increased attention of the scientific community. The adoption of Advanced Oxidation Processes (AOPs) represents an efficient strategy to remove recalcitrant compounds from aqueous streams and achieve high mineralization levels. Amongst AOPs, the photo Fenton process has been widely investigated due to the possibility of using a renewable energy source (i.e. solar energy) and low concentration of catalyst. On the other hand, the use of photo Fenton process is restricted to acidic pH values, with associate high operating costs for industrial scale applications. To overcome these drawbacks, photo Fenton processes modified by adding selected chelating agents can be successfully performed at neutral pH. The present review aims at examining and comparing the most relevant papers dealing with photo Fenton processes at neutral pH that appeared in the literature so far. Such papers were classified by chelating species adopted. In particular, for each iron(III) ligand complex, the mechanism of photolysis, the speciation diagram, the light absorption properties, the quantum yields, biodegradation and toxicity, and some example of applications are reported. As a conclusion, suitable criteria for choosing chelating agent and operating conditions in photo Fenton processes at neutral pH are proposed.
Laura Clarizia Danilo Russo Ilaria Di Somma Raffaele Marotta Roberto Andreozzi