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 1101–1150 of 1589 papers

TitleStatusHype
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution—0
Deep Image Super Resolution via Natural Image Priors—0
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution—0
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network—0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
Deep Artifact-Free Residual Network for Single Image Super-Resolution—0
Perception-Oriented Stereo Image Super-Resolution—0
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks—0
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow—0
Perceptual Fairness in Image Restoration—0
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution—0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution—0
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution—0
Perceptual Video Super Resolution with Enhanced Temporal Consistency—0
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution—0
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics—0
CUF: Continuous Upsampling Filters—0
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution—0
CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution—0
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery—0
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution—0
CT-image Super Resolution Using 3D Convolutional Neural Network—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution—0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—0
Across-scale Process Similarity based Interpolation for Image Super-Resolution—0
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution—0
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information—0
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images—0
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution—0
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis—0
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis—0
A Convex Approach for Image Hallucination—0
Process of image super-resolution—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
Progressive Generative Adversarial Networks for Medical Image Super resolution—0
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations—0
Progressively Unfreezing Perceptual GAN—0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception—0
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution—0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Proximal Splitting Networks for Image Restoration—0
PTSR: Patch Translator for 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