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

Showing 901925 of 1589 papers

TitleStatusHype
Learning Two-factor Representation for Magnetic Resonance Image Super-resolution0
Learning with Privileged Information for Efficient Image Super-Resolution0
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution0
LFMamba: Light Field Image Super-Resolution with State Space Model0
Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution0
LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction0
License Plate Super-Resolution Using Diffusion Models0
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild0
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment0
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration0
Effective Invertible Arbitrary Image Rescaling0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning0
Training a Task-Specific Image Reconstruction Loss0
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction0
E-FCNN for tiny facial expression recognition0
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network0
Lightweight single-image super-resolution network based on dual paths0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution0
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction0
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution0
Dual Recovery Network with Online Compensation for Image Super-Resolution0
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified