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
Image Super-resolution with An Enhanced Group Convolutional Neural NetworkCode1
Image Super-Resolution with Deep DictionaryCode1
Image Super-Resolution With Non-Local Sparse AttentionCode1
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Edge-enhanced Feature Distillation Network for Efficient Super-ResolutionCode1
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale LearningCode1
Face Super-Resolution Through Wasserstein GANsCode1
Accurate Image Restoration with Attention Retractable TransformerCode1
Best-Buddy GANs for Highly Detailed Image Super-ResolutionCode1
Dual-Stage Approach Toward Hyperspectral Image Super-ResolutionCode1
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-ResolutionCode1
Dual Super-Resolution Learning for Semantic SegmentationCode1
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time seriesCode1
DREAM: Diffusion Rectification and Estimation-Adaptive ModelsCode1
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
Bayesian Image Reconstruction using Deep Generative ModelsCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
DSR: Towards Drone Image Super-ResolutionCode1
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient 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