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 426450 of 1589 papers

TitleStatusHype
Blind Image Super-resolution with Rich Texture-Aware Codebooks0
Local Statistics for Generative Image Detection0
Unpaired MRI Super Resolution with Contrastive Learning0
PTSR: Patch Translator for Image Super-Resolution0
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution0
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder ApproachCode1
Video Super-Resolution Using a Grouped Residual in Residual Network0
Image super-resolution via dynamic networkCode1
Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling0
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics0
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super ResolutionCode0
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image SynthesisCode0
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis0
Multi-Depth Branch Network for Efficient Image Super-ResolutionCode1
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN0
Data Upcycling Knowledge Distillation for Image Super-ResolutionCode0
Adaptation of the super resolution SOTA for Art Restoration in camera capture imagesCode0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function0
License Plate Super-Resolution Using Diffusion Models0
Frequency Estimation Using Complex-Valued Shifted Window TransformerCode1
Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-ResolutionCode1
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool0
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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
8SwinFIRPSNR29.36Unverified
9CPAT+PSNR29.36Unverified
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