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 926950 of 1589 papers

TitleStatusHype
Blind Image Super-Resolution via Contrastive Representation Learning0
Text Prior Guided Scene Text Image Super-resolutionCode1
Distilling the Knowledge from Conditional Normalizing FlowsCode0
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
Image Super-Resolution With Non-Local Sparse AttentionCode1
Scene Text Telescope: Text-Focused Scene Image Super-ResolutionCode0
Practical Single-Image Super-Resolution Using Look-Up TableCode1
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network0
One-to-many Approach for Improving Super-ResolutionCode1
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution0
Feedback Pyramid Attention Networks for Single Image Super-Resolution0
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV MinimizationCode1
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution0
Task Transformer Network for Joint MRI Reconstruction and Super-ResolutionCode1
Variational AutoEncoder for Reference based Image Super-ResolutionCode1
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks0
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-ResolutionCode1
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity EstimationCode1
Fourier Space Losses for Efficient Perceptual Image Super-Resolution0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising0
Self-Organized Residual Blocks for Image Super-Resolution0
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed TomographyCode1
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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