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
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems0
Medical image super-resolution method based on dense blended attention network0
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution0
Metric Imitation by Manifold Transfer for Efficient Vision Applications0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference0
Mitigating Channel-wise Noise for Single Image Super Resolution0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery0
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors0
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network0
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution0
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images0
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution0
More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination0
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution0
MPSI: Mamba enhancement model for pixel-wise sequential interaction Image Super-Resolution0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network0
MRI Super-Resolution with Ensemble Learning and Complementary Priors0
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better0
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution0
MSRA-SR: Image Super-resolution Transformer with Multi-scale Shared Representation Acquisition0
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution0
Multi-Attention Generative Adversarial Network for Remote Sensing Image 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
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