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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.
Exploration of Lightweight Single Image Denoising with Transformers and Truly Fair Training
Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising
Tunable Convolutions with Parametric Multi-Loss Optimization
HDR Imaging with Spatially Varying Signal-to-Noise Ratios
Masked Autoencoders as Image Processors
Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration
Real-time Controllable Denoising for Image and Video
Whole-body PET image denoising for reduced acquisition time
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning
Pushing The Limits of the Wiener Filter in Image Denoising
Masked Image Training for Generalizable Deep Image Denoising
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data
Hybrid Spectral Denoising Transformer with Guided Attention
SUD$^2$: Supervision by Denoising Diffusion Models for Image Reconstruction
Xformer: Hybrid X-Shaped Transformer for Image Denoising
Blind2Sound: Self-Supervised Image Denoising without Residual Noise
KBNet: Kernel Basis Network for Image Restoration
Cloud K-SVD for Image Denoising
Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising
Spatial-Frequency Attention for Image Denoising
LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring
An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis
Low Latency Video Denoising for Online Conferencing Using CNN Architectures
Explicit Diffusion of Gaussian Mixture Model Based Image Priors
A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks
QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Diffusion Model for Generative Image Denoising
Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond
NoiseTransfer: Image Noise Generation with Contrastive Embeddings
Mixed Attention Network for Hyperspectral Image Denoising
Image Restoration with Mean-Reverting Stochastic Differential Equations
Removing Structured Noise with Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.