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 1301–1350 of 1589 papers

TitleStatusHype
XCycles Backprojection Acoustic Super-Resolution—0
Dynamic Attention-Guided Diffusion for Image Super-Resolution—0
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO—0
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study—0
ODE-Inspired Network Design for Single Image Super-Resolution—0
Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples—0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
On-Device Text Image Super Resolution—0
One Model for Two Tasks: Cooperatively Recognizing and Recovering Low-Resolution Scene Text Images by Iterative Mutual Guidance—0
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution—0
On training deep networks for satellite image super-resolution—0
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution—0
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution—0
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution—0
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation—0
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network—0
Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks—0
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization—0
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction—0
Orthogonally Regularized Deep Networks For Image Super-resolution—0
PAON: A New Neuron Model using Padé Approximants—0
ParaDiS: Parallelly Distributable Slimmable Neural Networks—0
Parameter-Free Channel Attention for Image Classification and Super-Resolution—0
PartDiff: Image Super-resolution with Partial Diffusion Models—0
Patch-based image Super Resolution using generalized Gaussian mixture model—0
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment—0
Perception-Oriented Stereo Image Super-Resolution—0
Perceptual Fairness in Image Restoration—0
Perceptual Video Super Resolution with Enhanced Temporal Consistency—0
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics—0
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information—0
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis—0
Process of image super-resolution—0
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging SystemsCode0
Deep Laplacian Pyramid Network for Text Images Super-ResolutionCode0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution NetworkCode0
Non-Local Recurrent Network for Image RestorationCode0
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-ResolutionCode0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
Deep Fourier Up-SamplingCode0
Neural Nearest Neighbors NetworksCode0
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical PriorCode0
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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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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