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 251275 of 1589 papers

TitleStatusHype
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World ScenesCode1
Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion ModelsCode1
Image Super-Resolution With Non-Local Sparse AttentionCode1
GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain ConnectomesCode1
Accurate Image Restoration with Attention Retractable TransformerCode1
Best-Buddy GANs for Highly Detailed Image Super-ResolutionCode1
Fourier Series Expansion Based Filter Parametrization for Equivariant ConvolutionsCode1
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-ResolutionCode1
Flow-based Kernel Prior with Application to Blind Super-ResolutionCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-ResolutionCode1
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity EstimationCode1
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
Bayesian Image Reconstruction using Deep Generative ModelsCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
Flexible Style Image Super-Resolution using Conditional ObjectiveCode1
Frequency Domain-based Perceptual Loss for Super ResolutionCode1
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
Deep Burst Super-ResolutionCode1
Fast Nearest Convolution for Real-Time Efficient Image Super-ResolutionCode1
Feature Distillation Interaction Weighting Network for Lightweight Image Super-ResolutionCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
A heterogeneous group CNN for image super-resolutionCode1
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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