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 326350 of 1589 papers

TitleStatusHype
Fully Quantized Image Super-Resolution NetworksCode1
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile DevicesCode1
A Systematic Survey of Deep Learning-based Single-Image Super-ResolutionCode1
Efficient scene text image super-resolution with semantic guidanceCode1
Gradient Variance Loss for Structure-Enhanced Image Super-ResolutionCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale LearningCode1
Cross-View Hierarchy Network for Stereo Image Super-ResolutionCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
Hierarchical Residual Attention Network for Single Image Super-ResolutionCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
Designing a Practical Degradation Model for Deep Blind Image Super-ResolutionCode1
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
Deep Learning-based Face Super-Resolution: A SurveyCode1
Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient ConvolutionCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-ResolutionCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and ReconstructionCode1
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter EstimationCode1
Detail-Preserving Transformer for Light Field Image Super-ResolutionCode1
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time seriesCode1
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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