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Image Denoising
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Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.
<span style="color:grey; opacity: 0.6">( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video Enhancement
Noise Synthesis for Low-Light Image Denoising with Diffusion Models
Noise2Score3D: Tweedie's Approach for Unsupervised Point Cloud Denoising
Denoising via Repainting: an image denoising method using layer wise medical image repainting
Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images
Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising
Geodesic Diffusion Models for Medical Image-to-Image Generation
PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems
Noise2Score3D:Unsupervised Tweedie's Approach for Point Cloud Denoising
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation
Denoising Designs-inherited Search Framework for Image Denoising
A Data-Driven Paradigm-Based Image Denoising and Mosaicking Approach for High-Resolution Acoustic Camera
Is Noise Conditioning Necessary for Denoising Generative Models?
Exploiting network optimization stability for enhanced PET image denoising using deep image prior
Residual Transformer Fusion Network for Salt and Pepper Image Denoising
Prompt-SID: Learning Structural Representation Prompt via Latent Diffusion for Single-Image Denoising
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems
Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT Image Denoising
Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Boostrapping
Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising
Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images
CNN-based TEM image denoising from first principles
Cloud Removal With PolSAR-Optical Data Fusion Using A Two-Flow Residual Network
InDeed: Interpretable image deep decomposition with guaranteed generalizability
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion
MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising
Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising
Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation
Personalized Generative Low-light Image Denoising and Enhancement
Probabilities-Informed Machine Learning
Bilevel Learning with Inexact Stochastic Gradients
OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems
LAN: Learning to Adapt Noise for Image Denoising
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.