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 10511075 of 1589 papers

TitleStatusHype
On-Device Text Image Super Resolution0
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature LearningCode1
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets0
Dense U-net for super-resolution with shuffle pooling layer0
EPSR: Edge Profile Super resolution0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain0
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution0
Symmetric Parallax Attention for Stereo Image Super-ResolutionCode1
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction0
A Novel Fast 3D Single Image Super-Resolution Algorithm0
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image RestorationCode1
Differentiable Channel Sparsity Search via Weight Sharing within Filters0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference0
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network0
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Attaining Real-Time Super-Resolution for Microscopic Images Using GANCode1
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution0
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-ResolutionCode0
Efficient Image Super-Resolution Using Pixel AttentionCode1
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network0
FAN: Frequency Aggregation Network for Real Image Super-resolution0
Interpretable Detail-Fidelity Attention Network for Single Image Super-ResolutionCode1
Deep Artifact-Free Residual Network for Single Image Super-Resolution0
Blind Image Super-Resolution with Spatial Context Hallucination0
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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