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 201225 of 1589 papers

TitleStatusHype
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
Efficient Image Super-Resolution using Vast-Receptive-Field AttentionCode1
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient ConvolutionCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical FlowCode1
Detail-Preserving Transformer for Light Field Image Super-ResolutionCode1
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Enhanced Deep Residual Networks for Single Image Super-ResolutionCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Enhanced Semantic Extraction and Guidance for UGC Image Super ResolutionCode1
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World ScenesCode1
Deep Unfolding Network for Image Super-ResolutionCode1
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolutionCode1
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Component Divide-and-Conquer for Real-World Image Super-ResolutionCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Face Super-Resolution Through Wasserstein GANsCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
Show:102550
← PrevPage 9 of 64Next →

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