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 851–875 of 1589 papers

TitleStatusHype
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
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
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
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection—0
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
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution—0
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution—0
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge—0
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense NetworkCode0
Quantum Annealing for Single Image Super-Resolution—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement—0
Image-to-image domain adaptation for vehicle re-identification—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—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