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 51–100 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-Resolution—0
PIDSR: Complementary Polarized Image Demosaicing and Super-ResolutionCode1
BUFF: Bayesian Uncertainty Guided Diffusion Probabilistic Model for Single Image Super-Resolution—0
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical PriorCode0
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution—0
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images OnlyCode0
Deterministic Medical Image Translation via High-fidelity Brownian Bridges—0
Progressive Focused Transformer for Single Image Super-ResolutionCode2
Burst Image Super-Resolution with Mamba—0
L^2FMamba: Lightweight Light Field Image Super-Resolution with State Space Model—0
Single-Step Latent Consistency Model for Remote Sensing Image Super-Resolution—0
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality AssessmentCode1
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model—0
Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-ResolutionCode0
Toward task-driven satellite image super-resolution—0
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution—0
CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution—0
The Power of Context: How Multimodality Improves Image Super-Resolution—0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network—0
Rethinking Image Evaluation in Super-Resolution—0
C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales—0
RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall DownscalingCode0
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
FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution—0
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
MegaSR: Mining Customized Semantics and Expressive Guidance for Image Super-ResolutionCode1
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution—0
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios—0
CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-ResolutionCode3
Emulating Self-attention with Convolution for Efficient Image Super-ResolutionCode2
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-ResolutionCode1
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution—0
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual LearningCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization—0
BadRefSR: Backdoor Attacks Against Reference-based Image Super ResolutionCode0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—0
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-ResolutionCode1
MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge Distillation—0
Data-driven Super-Resolution of Flood Inundation Maps using Synthetic SimulationsCode0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Heterogeneous Mixture of Experts for Remote Sensing Image Super-ResolutionCode1
Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric PriorsCode0
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory DistillationCode3
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers—0
Exploring Linear Attention Alternative for Single Image Super-ResolutionCode0
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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