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 776800 of 1589 papers

TitleStatusHype
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction0
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer0
BSRAW: Improving Blind RAW Image Super-Resolution0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements0
Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image InformationCode0
EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset0
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-ResolutionCode0
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network0
J-Net: Improved U-Net for Terahertz Image Super-Resolution0
Generative Powers of Ten0
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution0
Infrared Image Super-Resolution via GAN0
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution0
SinSR: Diffusion-Based Image Super-Resolution in a Single StepCode0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution0
Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution0
LATIS: Lambda Abstraction-based Thermal Image Super-resolution0
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution0
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics0
Texture and Noise Dual Adaptation for Infrared Image Super-ResolutionCode0
The Perception-Robustness Tradeoff in Deterministic Image Restoration0
OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-ResolutionCode0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
Efficient Model-Based Deep Learning via Network Pruning and Fine-TuningCode0
Exploring Deep Learning Image Super-Resolution for Iris Recognition0
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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