SOTAVerified

Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

Showing 11261150 of 1589 papers

TitleStatusHype
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution0
CT-image Super Resolution Using 3D Convolutional Neural Network0
PNEN: Pyramid Non-Local Enhanced Networks0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning0
Across-scale Process Similarity based Interpolation for Image Super-Resolution0
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution0
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information0
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images0
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution0
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis0
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis0
A Convex Approach for Image Hallucination0
Process of image super-resolution0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network0
Progressive Generative Adversarial Networks for Medical Image Super resolution0
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations0
Progressively Unfreezing Perceptual GAN0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution0
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception0
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks0
Proximal Splitting Networks for Image Restoration0
PTSR: Patch Translator for Image Super-Resolution0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified