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 12761300 of 1589 papers

TitleStatusHype
Binary Diffusion Probabilistic Model0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing0
A comparative analysis of SRGAN models0
Sequence Matters: Harnessing Video Models in 3D Super-Resolution0
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution0
Seven ways to improve example-based single image super resolution0
Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network0
Binarized Neural Network for Single Image Super Resolution0
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation0
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning0
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution0
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution0
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network0
Simultaneous Super-Resolution of Depth and Images Using a Single Camera0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution0
Bi-GANs-ST for Perceptual Image Super-resolution0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior0
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder0
Single Image Super-Resolution0
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution0
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution0
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss0
Single Image Super-Resolution Based on Global-Local Information Synergy0
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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