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

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Showing 951–1000 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
Learning Two-factor Representation for Magnetic Resonance Image Super-resolution—0
Learning with Privileged Information for Efficient Image Super-Resolution—0
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution—0
LFMamba: Light Field Image Super-Resolution with State Space Model—0
LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction—0
License Plate Super-Resolution Using Diffusion Models—0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network—0
Lightweight single-image super-resolution network based on dual paths—0
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction—0
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution—0
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning—0
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment—0
Local Patch Encoding-Based Method for Single Image Super-Resolution—0
Local-Selective Feature Distillation for Single Image Super-Resolution—0
LocalSR: Image Super-Resolution in Local Region—0
Local Statistics for Generative Image Detection—0
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models—0
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge Distillation—0
Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning—0
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping—0
Matrix Neural Networks—0
Matrix Variate RBM and Its Applications—0
MaxSR: Image Super-Resolution Using Improved MaxViT—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