A review on Single Image Super Resolution techniques using generative adversarial network
K Singla, R Pandey, U Ghanekar - Optik, 2022 - Elsevier
Abstract Single Image Super Resolution (SISR) is a process to obtain a high pixel density
and refined details from a low resolution (LR) image to get upscaled and sharper high …
and refined details from a low resolution (LR) image to get upscaled and sharper high …
Unsupervised degradation representation learning for blind super-resolution
Most existing CNN-based super-resolution (SR) methods are developed based on an
assumption that the degradation is fixed and known (eg, bicubic downsampling). However …
assumption that the degradation is fixed and known (eg, bicubic downsampling). However …
Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications
Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving
computational imaging problems through the integration of physical models and learned …
computational imaging problems through the integration of physical models and learned …
Deep constrained least squares for blind image super-resolution
In this paper, we tackle the problem of blind image super-resolution (SR) with a reformulated
degradation model and two novel modules. Following the common practices of blind SR, our …
degradation model and two novel modules. Following the common practices of blind SR, our …
Blind image super-resolution: A survey and beyond
Blind image super-resolution (SR), aiming to super-resolve low-resolution images with
unknown degradation, has attracted increasing attention due to its significance in promoting …
unknown degradation, has attracted increasing attention due to its significance in promoting …
Mutual affine network for spatially variant kernel estimation in blind image super-resolution
Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially
invariant across the whole image. However, such an assumption is rarely applicable for real …
invariant across the whole image. However, such an assumption is rarely applicable for real …
Efficient test-time adaptation for super-resolution with second-order degradation and reconstruction
Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-
resolution (HR) using paired HR-LR training images. Conventional SR methods typically …
resolution (HR) using paired HR-LR training images. Conventional SR methods typically …
Best-buddy gans for highly detailed image super-resolution
We consider the single image super-resolution (SISR) problem, where a high-resolution
(HR) image is generated based on a low-resolution (LR) input. Recently, generative …
(HR) image is generated based on a low-resolution (LR) input. Recently, generative …
Learning correction filter via degradation-adaptive regression for blind single image super-resolution
Although existing image deep learning super-resolution (SR) methods achieve promising
performance on benchmark datasets, they still suffer from severe performance drops when …
performance on benchmark datasets, they still suffer from severe performance drops when …
Deep internal learning: Deep learning from a single input
Deep learning, in general, focuses on training a neural network from large labeled datasets.
Yet, in many cases, there is value in training a network just from the input at hand. This is …
Yet, in many cases, there is value in training a network just from the input at hand. This is …