Denoising
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Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.
<span style="color:grey; opacity: 0.6">( Image credit: Beyond a Gaussian Denoiser )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HyelinNAM/ContrastiveDenoisingScore
Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing
https://github.com/HyemiEsme/PUCA
PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising
https://github.com/IDKiro/CBDNet-tensorflow
https://github.com/IMAC-projects/Deblurring-PyTorch
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
https://github.com/IMAC-projects/SRN-Deblurring-PyTorch
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
https://github.com/IPL-UV/gauss4eo
https://github.com/IPL-UV/rbig
https://github.com/IPL-UV/rbig_matlab
https://github.com/IlyaKodua/dncnn
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/Ipsedo/MusicDiffusion
https://github.com/IshaFaodail/DeepCNN_denoising
https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement
https://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models
https://github.com/Jentuuh/edge-preserving-diffusion
https://github.com/Jia-Research-Lab/Simple-SR
https://github.com/Jiaming-Liu/BayerUnifyAug
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
https://github.com/JimmyChame/SADNet
https://github.com/JonasLoos/h-space-directions
Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models
https://github.com/JuliaTomo/DeepImagePrior.jl
https://github.com/JuliaTomo/DeepReconstruction.jl
https://github.com/JuliaWolleb/diffusion-anomaly
https://github.com/KAIST-Visual-AI-Group/SyncTweedies
SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
https://github.com/Kano-Wu/Domain-Adversarial-Neural-Networks
https://github.com/KiUngSong/Generative-Models
https://github.com/KingJamesSong/DifferentiableSVD
https://github.com/KunStats/Paddle-DIP
https://github.com/KushGrandhi/Polaroid
https://github.com/L4TTiCe/SAR2SAR
SAR2SAR: a semi-supervised despeckling algorithm for SAR images
https://github.com/L4TTiCe/SAR2SAR_v2
SAR2SAR: a semi-supervised despeckling algorithm for SAR images
https://github.com/Lei-IT/GMAMP
https://github.com/Lillian1082/IRAE_pytorch
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
https://github.com/Longer430/low_dose_CT
https://github.com/LouisRouss/Diffusion-Based-Model-for-Colorization
https://github.com/LucasElbert/FFDNet
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/Lucien66/LIPT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.