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 601650 of 1589 papers

TitleStatusHype
Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic IndexesCode0
Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolutionCode0
Scene Text Telescope: Text-Focused Scene Image Super-ResolutionCode0
Densely Residual Laplacian Super-ResolutionCode0
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation NetworksCode0
NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-ResolutionCode0
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold DiscriminationCode0
Multi-scale Residual Network for Image Super-ResolutionCode0
Multi-scale deep neural networks for real image super-resolutionCode0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical PriorCode0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal imagesCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled DictionariesCode0
Blind Super-Resolution With Iterative Kernel CorrectionCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Multimodal Sensor Fusion In Single Thermal image Super-ResolutionCode0
DeepRED: Deep Image Prior Powered by REDCode0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution NetworkCode0
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving TransformerCode0
MemNet: A Persistent Memory Network for Image RestorationCode0
Deep Mean-Shift Priors for Image RestorationCode0
Deeply-Recursive Convolutional Network for Image Super-ResolutionCode0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-ResolutionCode0
Neural Nearest Neighbors NetworksCode0
Deep Learning for Single Image Super-Resolution: A Brief ReviewCode0
Deep Learning for Multiple-Image Super-ResolutionCode0
Binary Document Image Super Resolution for Improved Readability and OCR PerformanceCode0
Accurate Image Super-Resolution Using Very Deep Convolutional NetworksCode0
Deep Learning for Image Super-resolution: A SurveyCode0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
A Matrix-in-matrix Neural Network for Image Super ResolutionCode0
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual QualityCode0
Localized Super Resolution for Foreground Images using U-Net and MR-CNNCode0
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
Deep Learning-Based Channel EstimationCode0
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimationCode0
Deep Laplacian Pyramid Networks for Fast and Accurate Super-ResolutionCode0
Deep Laplacian Pyramid Network for Text Images Super-ResolutionCode0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images OnlyCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolutionCode0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
Deep Fourier Up-SamplingCode0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
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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