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 751775 of 1589 papers

TitleStatusHype
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention0
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality AssessmentCode0
Cross-domain heterogeneous residual network for single image super-resolutionCode0
Diverse super-resolution with pretrained deep hiererarchical VAEs0
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling0
Semantically Accurate Super-Resolution Generative Adversarial Networks0
Residual Local Feature Network for Efficient Super-ResolutionCode1
Blueprint Separable Residual Network for Efficient Image Super-ResolutionCode1
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and ResultsCode1
Activating More Pixels in Image Super-Resolution TransformerCode3
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution0
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-ResolutionCode1
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive TransformerCode1
Generative Adversarial Networks for Image Super-Resolution: A Survey0
IMDeception: Grouped Information Distilling Super-Resolution Network0
A New Dataset and Transformer for Stereoscopic Video Super-ResolutionCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results0
Edge-enhanced Feature Distillation Network for Efficient Super-ResolutionCode1
Self-Calibrated Efficient Transformer for Lightweight Super-ResolutionCode1
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-ResolutionCode1
NAFSSR: Stereo Image Super-Resolution Using NAFNetCode4
Cylin-Painting: Seamless 360 Panoramic Image Outpainting and BeyondCode1
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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