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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.
An Operator-Splitting Method for the Gaussian Curvature Regularization Model with Applications to Surface Smoothing and Imaging
Physics-based Noise Modeling for Extreme Low-light Photography
Del-Net: A Single-Stage Network for Mobile Camera ISP
DCT2net: an interpretable shallow CNN for image denoising
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement
Adaptive Denoising via GainTuning
A 51.3 TOPS/W, 134.4 GOPS In-memory Binary Image Filtering in 65nm CMOS
Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices
Meta-Optimization of Deep CNN for Image Denoising Using LSTM
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery
Details Preserving Deep Collaborative Filtering-Based Method for Image Denoising
Dense-Sparse Deep Convolutional Neural Networks Training for Image Denoising
Collaborative Filtering-Based Method for Low-Resolution and Details Preserving Image Denoising
Deep Unfolding with Normalizing Flow Priors for Inverse Problems
Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging
Multi-modal and frequency-weighted tensor nuclear norm for hyperspectral image denoising
Controllable Confidence-Based Image Denoising
Inverting Adversarially Robust Networks for Image Synthesis
Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images
TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising
Uformer: A General U-Shaped Transformer for Image Restoration
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising
Stable and Interpretable Unrolled Dictionary Learning
On Hamilton-Jacobi PDEs and image denoising models with certain non-additive noise
Hyperspectral Image Denoising with Log-Based Robust PCA
SSCAN: A Spatial-spectral Cross Attention Network for Hyperspectral Image Denoising
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond
End-to-End Unsupervised Document Image Blind Denoising
Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Window-Level is a Strong Denoising Surrogate
HINet: Half Instance Normalization Network for Image Restoration
Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network
LINN: Lifting Inspired Invertible Neural Network for Image Denoising
Blind microscopy image denoising with a deep residual and multiscale encoder/decoder network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.