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 351–400 of 1589 papers

TitleStatusHype
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback—0
See More Details: Efficient Image Super-Resolution by Experts MiningCode2
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation—0
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow—0
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing—0
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution—0
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution—0
LKFormer: Large Kernel Transformer for Infrared Image Super-ResolutionCode1
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Efficient Image Super-Resolution via Symmetric Visual Attention Network—0
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token DictionaryCode2
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy—0
Transforming Image Super-Resolution: A ConvFormer-based Efficient ApproachCode2
Learning Large-Factor EM Image Super-Resolution with Generative PriorsCode1
CFAT: Unleashing Triangular Windows for Image Super-resolutionCode2
Image Processing GNN: Breaking Rigidity in Super-ResolutionCode0
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Towards Progressive Multi-Frequency Representation for Image WarpingCode0
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolutionCode0
Diffusion Models, Image Super-Resolution And Everything: A Survey—0
Compressing Deep Image Super-resolution Models—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
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 reconstruction—0
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer—0
BSRAW: Improving Blind RAW Image Super-ResolutionCode0
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 Requirements—0
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 Dataset—0
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-Resolution—0
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network—0
Generative Powers of Ten—0
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution—0
Spatial-Temporal Contrasting for Fine-Grained Urban Flow InferenceCode1
Infrared Image Super-Resolution via GAN—0
DREAM: Diffusion Rectification and Estimation-Adaptive ModelsCode1
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution—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