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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–525 of 1589 papers

TitleStatusHype
TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-ResolutionCode1
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration—0
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions—0
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection—0
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality AssessmentCode0
A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-ResolutionCode1
Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and ReportCode1
Towards Real-Time 4K Image Super-ResolutionCode1
Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models—0
Physics-Informed Ensemble Representation for Light-Field Image Super-ResolutionCode0
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images—0
Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution—0
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution LayersCode1
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
Learning from Multi-Perception Features for Real-Word Image Super-resolution—0
High-Similarity-Pass Attention for Single Image Super-Resolution—0
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution—0
A Dive into SAM Prior in Image Restoration—0
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Exploiting Diffusion Prior for Real-World Image Super-ResolutionCode4
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54—Unverified
2HMA†PSNR29.51—Unverified
3Hi-IR-LPSNR29.49—Unverified
4HAT-LPSNR29.47—Unverified
5HAT_FIRPSNR29.44—Unverified
6DRCTPSNR29.4—Unverified
7HATPSNR29.38—Unverified
8CPAT+PSNR29.36—Unverified
9SwinFIRPSNR29.36—Unverified
10CPATPSNR29.34—Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16—Unverified
2HMA†PSNR28.13—Unverified
3Hi-IR-LPSNR28.13—Unverified
4HAT-LPSNR28.09—Unverified
5HAT_FIRPSNR28.07—Unverified
6CPAT+PSNR28.06—Unverified
7DRCTPSNR28.06—Unverified
8HATPSNR28.05—Unverified
9CPATPSNR28.04—Unverified
10SwinFIRPSNR28.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72—Unverified
2DRCT-LPSNR28.7—Unverified
3HMA†PSNR28.69—Unverified
4HAT-LPSNR28.6—Unverified
5HAT_FIRPSNR28.43—Unverified
6DRCTPSNR28.4—Unverified
7HATPSNR28.37—Unverified
8CPAT+PSNR28.33—Unverified
9CPATPSNR28.22—Unverified
10PFTPSNR28.2—Unverified