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 751–800 of 1589 papers

TitleStatusHype
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction—0
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution—0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
Text-guided Explorable Image Super-resolution—0
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Generative Adversarial Models for Extreme Geospatial Downscaling—0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback—0
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
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Efficient Image Super-Resolution via Symmetric Visual Attention Network—0
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy—0
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolutionCode0
Towards Progressive Multi-Frequency Representation for Image WarpingCode0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
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
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-Resolution—0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements—0
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
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-ResolutionCode0
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network—0
J-Net: Improved U-Net for Terahertz Image Super-Resolution—0
Generative Powers of Ten—0
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution—0
Infrared Image Super-Resolution via GAN—0
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution—0
SinSR: Diffusion-Based Image Super-Resolution in a Single StepCode0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution—0
LATIS: Lambda Abstraction-based Thermal Image Super-resolution—0
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution—0
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics—0
Texture and Noise Dual Adaptation for Infrared Image Super-ResolutionCode0
The Perception-Robustness Tradeoff in Deterministic Image Restoration—0
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 Recognition—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