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 301325 of 1589 papers

TitleStatusHype
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous DatasetsCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
Image Super-Resolution via Iterative RefinementCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-ResolutionCode1
Hyperspectral Image Super Resolution with Real Unaligned RGB GuidanceCode1
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
Cross-View Hierarchy Network for Stereo Image Super-ResolutionCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Deep Unfolding Network for Image Super-ResolutionCode1
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale ApproachCode1
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time seriesCode1
Does Diffusion Beat GAN in Image Super Resolution?Code1
Accurate Image Restoration with Attention Retractable TransformerCode1
Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical NaturalnessCode1
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-ResolutionCode1
Deep Burst Super-ResolutionCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-ResolutionCode1
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-ResolutionCode1
Cross-Scale Internal Graph Neural Network 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
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