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 10011025 of 1589 papers

TitleStatusHype
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images0
Transformers in Vision: A Survey0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind 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
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques0
DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution0
Transport-based analysis, modeling, and learning from signal and data distributions0
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution0
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach0
Differentiable Search for Finding Optimal Quantization Strategy0
Triple Attention Mixed Link Network for Single Image Super Resolution0
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
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation0
Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation0
Differentiable Channel Sparsity Search via Weight Sharing within Filters0
Hypernetwork functional image representation0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation0
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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