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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.
Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix
Scaling Laws For Deep Learning Based Image Reconstruction
Multi-stage image denoising with the wavelet transform
Enhancing convolutional neural network generalizability via low-rank weight approximation
Analog Image Denoising with an Adaptive Memristive Crossbar Network
Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization
De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data
Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising
Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image
Considering Image Information and Self-similarity: A Compositional Denoising Network
PET image denoising based on denoising diffusion probabilistic models
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising
A Multi-scale Video Denoising Algorithm for Raw Image
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks
Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising
Self-supervised training of deep denoisers in multi-coil MRI considering noise correlations
Learning Degradation Representations for Image Deblurring
A Topological Loss Function: Image Denoising on a Low-Light Dataset
Image denoising in acoustic field microscopy
Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions
Decay2Distill: Leveraging spatial perturbation and regularization for self-supervised image denoising
DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer
Real Image Restoration via Structure-preserving Complementarity Attention
Image Denoising Using Convolutional Autoencoder
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising
Deep Audio Waveform Prior
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling
Denoising single images by feature ensemble revisited
Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning
Polarized Color Image Denoising using Pocoformer
Efficient and Flexible Sublabel-Accurate Energy Minimization
Hypernetwork-Based Adaptive Image Restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.