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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–875 of 1589 papers

TitleStatusHype
A Dive into SAM Prior in Image Restoration—0
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution—0
Enhancing Frequency for Single Image Super-Resolution with Learnable Separable Kernels—0
Learning a Mixture of Deep Networks for Single Image Super-Resolution—0
Towards Robust Drone Vision in the Wild—0
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution—0
Learning-Based Quality Assessment for Image Super-Resolution—0
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods—0
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution—0
Enhance the Image: Super Resolution using Artificial Intelligence in MRI—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Learning Deep Analysis Dictionaries for Image Super-Resolution—0
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries—0
Enhanced generative adversarial network for 3D brain MRI super-resolution—0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks—0
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool—0
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution—0
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution—0
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution—0
Efficient Super Resolution Using Binarized Neural Network—0
Learning from Multi-Perception Features for Real-Word Image Super-resolution—0
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction—0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

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