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 …
Content-aware local gan for photo-realistic super-resolution
Recently, GAN has successfully contributed to making single-image super-resolution (SISR)
methods produce more realistic images. However, natural images have complex distribution …
methods produce more realistic images. However, natural images have complex distribution …
AND: Adversarial neural degradation for learning blind image super-resolution
Learnt deep neural networks for image super-resolution fail easily if the assumed
degradation model in training mismatches that of the real degradation source at the …
degradation model in training mismatches that of the real degradation source at the …
Learn from unpaired data for image restoration: A variational bayes approach
Collecting paired training data is difficult in practice, but the unpaired samples broadly exist.
Current approaches aim at generating synthesized training data from unpaired samples by …
Current approaches aim at generating synthesized training data from unpaired samples by …
Robustness and exploration of variational and machine learning approaches to inverse problems: An overview
This paper provides an overview of current approaches for solving inverse problems in
imaging using variational methods and machine learning. A special focus lies on point …
imaging using variational methods and machine learning. A special focus lies on point …
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution
Most of the recent literature on image Super-Resolution (SR) can be classified into two main
approaches. The first one involves learning a corruption model tailored to a specific dataset …
approaches. The first one involves learning a corruption model tailored to a specific dataset …
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Uncertainty quantification is critical for deploying deep neural networks (DNNs) in real-world
applications. An Auxiliary Uncertainty Estimator (AuxUE) is one of the most effective means …
applications. An Auxiliary Uncertainty Estimator (AuxUE) is one of the most effective means …
Fooling the Image Dehazing Models by First Order Gradient
The research on the single image dehazing task has been widely explored. However, as far
as we know, no comprehensive study has been conducted on the robustness of the well …
as we know, no comprehensive study has been conducted on the robustness of the well …
Deep generative model based rate-distortion for image downscaling assessment
In this paper we propose Image Downscaling Assessment by Rate-Distortion (IDA-RD) a
novel measure to quantitatively evaluate image downscaling algorithms. In contrast to image …
novel measure to quantitatively evaluate image downscaling algorithms. In contrast to image …
A novel deep learning technique for medical image analysis using improved optimizer
V Agarwal, MC Lohani, AS Bist - Health Informatics Journal, 2024 - journals.sagepub.com
Application of Convolutional neural network in spectrum of Medical image analysis are
providing benchmark outputs which converges the interest of many researchers to explore it …
providing benchmark outputs which converges the interest of many researchers to explore it …