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 10261050 of 1589 papers

TitleStatusHype
Multi-Attributed and Structured Text-to-Face Synthesis0
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection0
Multi-Contrast Super-Resolution MRI Through a Progressive Network0
Multi-frame image super-resolution with fast upscaling technique0
Multi-grained Attention Networks for Single Image Super-Resolution0
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution0
Multi-image Super-resolution via Quality Map Associated Attention Network0
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution0
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution0
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform0
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing0
Multimodal Deep Unfolding for Guided Image Super-Resolution0
Multimodal Image Super-resolution via Deep Unfolding with Side Information0
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach0
Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution0
Multi-scale Image Super Resolution with a Single Auto-Regressive Model0
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution0
Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation0
Hypernetwork functional image representation0
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution0
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS0
Neural Differential Equations for Single Image Super-resolution0
NLCUnet: Single-Image Super-Resolution Network with Hairline Details0
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy0
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based 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
8SwinFIRPSNR29.36Unverified
9CPAT+PSNR29.36Unverified
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