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Journal ArticleDOI

Image quality assessment: from error visibility to structural similarity

TLDR
In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Abstract
Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

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Proceedings ArticleDOI

Image-to-Image Translation with Conditional Adversarial Networks

TL;DR: Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Posted Content

Image-to-Image Translation with Conditional Adversarial Networks

TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Proceedings ArticleDOI

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

TL;DR: SRGAN as mentioned in this paper proposes a perceptual loss function which consists of an adversarial loss and a content loss, which pushes the 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.
Book ChapterDOI

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

TL;DR: In this paper, the authors combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image style transfer, where a feedforward network is trained to solve the optimization problem proposed by Gatys et al. in real-time.
Journal ArticleDOI

Image Super-Resolution Using Deep Convolutional Networks

TL;DR: Zhang et al. as discussed by the authors proposed a deep learning method for single image super-resolution (SR), which directly learns an end-to-end mapping between the low/high-resolution images.
References
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Proceedings ArticleDOI

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Journal ArticleDOI

The cortex transform: rapid computation of simulated neural images

TL;DR: A transform which maps an image into a set of images that vary in resolution and orientation, each pixel in the output may be regarded as the simulated response of a neuron in human visual cortex is described.
Journal ArticleDOI

Model of visual contrast gain control and pattern masking

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Journal ArticleDOI

Perceptual quality metrics applied to still image compression

TL;DR: A review of perceptual image quality metrics and their application to still image compression can be found in this article, where a broad range of metrics ranging from simple mathematical measures to those which incorporate full perceptual models are examined.
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