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 726–750 of 1589 papers

TitleStatusHype
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Blind Image Super-Resolution via Contrastive Representation Learning—0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
Blind Image Super-Resolution: A Survey and Beyond—0
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention—0
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization—0
Progressive Image Super-Resolution via Neural Differential Equation—0
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding—0
Analysis Operator Learning and Its Application to Image Reconstruction—0
An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution—0
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks—0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
BLADE: Filter Learning for General Purpose Computational Photography—0
Performance of a GPU- and Time-Efficient Pseudo 3D Network for Magnetic Resonance Image Super-Resolution and Motion Artifact Reduction—0
Large coordinate kernel attention network for lightweight image super-resolution—0
Large Receptive Field Networks for High-Scale Image Super-Resolution—0
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution—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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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