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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-Shot Enhancement of Low-Light Image Based on Retinex Decomposition
Image Restoration via Frequency Selection
Thermal Face Image Classification using Deep Learning Techniques
Product of Gaussian Mixture Diffusion Models
PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising
A cross Transformer for image denoising
Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising
Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion Model
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs
Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise
ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain
Generalization in diffusion models arises from geometry-adaptive harmonic representations
JPEG Information Regularized Deep Image Prior for Denoising
Controlling Vision-Language Models for Multi-Task Image Restoration
Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising
Uncertainty Quantification via Neural Posterior Principal Components
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks
Image Denoising via Style Disentanglement
On the Posterior Distribution in Denoising: Application to Uncertainty Quantification
Connecting Image Inpainting with Denoising in the Homogeneous Diffusion Setting
Self2Seg: Single-Image Self-Supervised Joint Segmentation and Denoising
Reconstruct-and-Generate Diffusion Model for Detail-Preserving Image Denoising
Mixed Graph Signal Analysis of Joint Image Denoising / Interpolation
All-optical image denoising using a diffractive visual processor
Wavelet-based Topological Loss for Low-Light Image Denoising
Hyperspectral Image Denoising via Self-Modulating Convolutional Neural Networks
CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions
HAT: Hybrid Attention Transformer for Image Restoration
Restoring Snow-Degraded Single Images With Wavelet in Vision Transformer
Multi-view Self-supervised Disentanglement for General Image Denoising
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
Sparse Models for Machine Learning
Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration
AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation
Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising
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