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 951–975 of 1589 papers

TitleStatusHype
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution—0
Learning a Mixture of Deep Networks for Single Image Super-Resolution—0
Learning-Based Quality Assessment for Image Super-Resolution—0
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Learning Deep Analysis Dictionaries for Image Super-Resolution—0
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution—0
Learning from Multi-Perception Features for Real-Word Image Super-resolution—0
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction—0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Learning Sparse Low-Precision Neural Networks With Learnable Regularization—0
Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling—0
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution—0
Learning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution—0
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding—0
Learning Parametric Sparse Models for Image Super-Resolution—0
Learning regularization and intensity-gradient-based fidelity for single image super resolution—0
Learning Resolution-Invariant Deep Representations for Person Re-Identification—0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
Learning Super-Resolution Jointly from External and Internal Examples—0
Learning To Zoom Inside Camera Imaging Pipeline—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