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 1076–1100 of 1589 papers

TitleStatusHype
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
LAConv: Local Adaptive Convolution for Image Fusion—0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank—0
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution—0
Image restoration quality assessment based on regional differential information entropy—0
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment—0
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution—0
Blind Image Super-Resolution: A Survey and Beyond—0
Impact of deep learning-based image super-resolution on binary signal detection—0
Blind Image Super-Resolution via Contrastive Representation Learning—0
Distilling the Knowledge from Conditional Normalizing FlowsCode0
Scene Text Telescope: Text-Focused Scene Image Super-ResolutionCode0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network—0
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution—0
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks—0
Fourier Space Losses for Efficient Perceptual Image Super-Resolution—0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising—0
Self-Organized Residual Blocks for Image 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