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Deep Learning for Image Super-resolution: A Survey
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Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.
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Cited by 4 Pith papers
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A multidimensional Gaussian loss with Fourier-represented covariances and an information-sharing regularizer lets a network emit closed-form correlated predictive distributions, demonstrated on wind-speed super-resolution.
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Super-resolution of Omnidirectional Images Using Adversarial Learning
A GAN-based super-resolution method for omnidirectional images, using a PatchGAN discriminator and a 360-SS loss, improves spherical quality metrics over SRGAN and bicubic.
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CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network
CRNet-A and CRNet-B unroll Convolutional Iterative Soft Thresholding into CNN layers for super-resolution and report competitive or better PSNR/SSIM than EDSR and RDN.
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Deriving a Quantitative Relationship Between Resolution and Human Classification Error
A two-parameter logistic curve relating MNIST image width in pixels to human classification error is fitted, but is presented without raw data, error bars, or validation.
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