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 13511375 of 1589 papers

TitleStatusHype
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold DiscriminationCode0
NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-ResolutionCode0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Multi-scale Residual Network for Image Super-ResolutionCode0
Multi-scale deep neural networks for real image super-resolutionCode0
Single Image Super-Resolution using Residual Channel Attention NetworkCode0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Binary Document Image Super Resolution for Improved Readability and OCR PerformanceCode0
Exemplar Guided Face Image Super-Resolution without Facial LandmarksCode0
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-ResolutionCode0
Evaluating Robustness of Deep Image Super-Resolution against Adversarial AttacksCode0
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
Ensemble Super-Resolution with A Reference DatasetCode0
Deep Burst DenoisingCode0
Texture and Noise Dual Adaptation for Infrared Image Super-ResolutionCode0
Deep Bi-Dense Networks for Image Super-ResolutionCode0
Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-ResolutionCode0
Multimodal Sensor Fusion In Single Thermal image Super-ResolutionCode0
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution NetworksCode0
OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-ResolutionCode0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled DictionariesCode0
Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic IndexesCode0
Accurate Image Super-Resolution Using Very Deep Convolutional NetworksCode0
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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