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 376400 of 1589 papers

TitleStatusHype
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features0
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-ResolutionCode2
Noise-free Optimization in Early Training Steps for Image Super-ResolutionCode1
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
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-ResolutionCode1
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements0
Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and DeblurringCode1
Image Restoration Through Generalized Ornstein-Uhlenbeck BridgeCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
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
TULIP: Transformer for Upsampling of LiDAR Point CloudsCode1
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-ResolutionCode0
Iterative Token Evaluation and Refinement for Real-World Super-ResolutionCode1
Training Neural Networks on RAW and HDR Images for Restoration TasksCode1
J-Net: Improved U-Net for Terahertz Image Super-Resolution0
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network0
Generative Powers of Ten0
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution0
Spatial-Temporal Contrasting for Fine-Grained Urban Flow InferenceCode1
Infrared Image Super-Resolution via GAN0
DREAM: Diffusion Rectification and Estimation-Adaptive ModelsCode1
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based 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