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 901–950 of 1589 papers

TitleStatusHype
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
Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution—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
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild—0
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment—0
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration—0
Effective Invertible Arbitrary Image Rescaling—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
Training a Task-Specific Image Reconstruction Loss—0
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction—0
E-FCNN for tiny facial expression recognition—0
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network—0
Lightweight single-image super-resolution network based on dual paths—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—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
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Dual Recovery Network with Online Compensation for Image Super-Resolution—0
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution—0
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
A deep primal-dual proximal network for image restoration—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
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models—0
AdderSR: Towards Energy Efficient Image Super-Resolution—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
Training Set Effect on Super Resolution for Automated Target Recognition—0
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks—0
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—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
DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors—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