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 5175 of 1589 papers

TitleStatusHype
Enhanced Semantic Extraction and Guidance for UGC Image Super ResolutionCode1
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution0
PIDSR: Complementary Polarized Image Demosaicing and Super-ResolutionCode1
BUFF: Bayesian Uncertainty Guided Diffusion Probabilistic Model for Single Image Super-Resolution0
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical PriorCode0
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images OnlyCode0
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution0
Deterministic Medical Image Translation via High-fidelity Brownian Bridges0
Progressive Focused Transformer for Single Image Super-ResolutionCode2
Burst Image Super-Resolution with Mamba0
L^2FMamba: Lightweight Light Field Image Super-Resolution with State Space Model0
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality AssessmentCode1
Single-Step Latent Consistency Model for Remote Sensing Image Super-Resolution0
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model0
Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-ResolutionCode0
Toward task-driven satellite image super-resolution0
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution0
CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution0
The Power of Context: How Multimodality Improves Image Super-Resolution0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network0
Rethinking Image Evaluation in Super-Resolution0
RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall DownscalingCode0
C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales0
QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-ResolutionCode1
Perceive, Understand and Restore: Real-World Image Super-Resolution with Autoregressive Multimodal Generative ModelsCode2
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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