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 1401–1425 of 1589 papers

TitleStatusHype
Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning—0
Super-resolution Guided Pore Detection for Fingerprint Recognition—0
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity—0
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network—0
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization—0
Super-resolution Reconstruction of Single Image for Latent features—0
Super-resolution Using Constrained Deep Texture Synthesis—0
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation—0
Super-Resolution with Deep Adaptive Image Resampling—0
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution—0
Super-resolved multi-temporal segmentation with deep permutation-invariant networks—0
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net—0
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data—0
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder—0
Supervised Deep Kriging for Single-Image Super-Resolution—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior—0
SwiftSRGAN -- Rethinking Super-Resolution for Efficient and Real-time Inference—0
Why Are Deep Representations Good Perceptual Quality Features?—0
A Novel Fast 3D Single Image Super-Resolution Algorithm—0
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps—0
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge—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