Papers with Code 아카이브
Image Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fanchimao/sunet
공식 코드https://github.com/fenghansen/pnnp
공식 코드Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising
https://github.com/fengzhang427/LRD
공식 코드https://github.com/fsherry/non-expansive-odes
공식 코드Designing Stable Neural Networks using Convex Analysis and ODEs
https://github.com/gbuzzard/generative-pnp-allerton
공식 코드Generative Plug and Play: Posterior Sampling for Inverse Problems
https://github.com/genforce/mganprior
공식 코드https://github.com/ghl1995/denoise-gan-in-cryo-EM
공식 코드Generative Adversarial Networks for Robust Cryo-EM Image Denoising
https://github.com/gladzhang/xformer
공식 코드https://github.com/goodaycoder/jdac
공식 코드Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI
https://github.com/google-research/maxim
공식 코드https://github.com/grishavak/LIDIA-denoiser
공식 코드LIDIA: Lightweight Learned Image Denoising with Instance Adaptation
https://github.com/grishavak/Patch_Ordering_as_a_Regularization_for_Inverse_Problems_in_Image_Processing
공식 코드Patch-Ordering as a Regularization for Inverse Problems in Image Processing
https://github.com/grishavak/pcst
공식 코드Patch-Craft Self-Supervised Training for Correlated Image Denoising
https://github.com/grokcv/groksar
공식 코드DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images
https://github.com/haihabi/generativecrb
공식 코드https://github.com/hao1635/lit-former
공식 코드https://github.com/haoqinhong/ccspnet-joint
공식 코드CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions
https://github.com/haoyuc/maskeddenoising
공식 코드Masked Image Training for Generalizable Deep Image Denoising
https://github.com/happycaoyue/PT-MWRN
공식 코드Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising
https://github.com/hellloxiaotian/ctnet
공식 코드https://github.com/hellloxiaotian/hwformer
공식 코드https://github.com/hellloxiaotian/mwdcnn
공식 코드https://github.com/helmholtz-ai-energy/hyde
공식 코드HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package
https://github.com/hkzhang91/hinas
공식 코드Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
https://github.com/huaqlili/prompt-sid
공식 코드https://github.com/hust-tan/diffusionvi
공식 코드Diffusion Priors for Variational Likelihood Estimation and Image Denoising
https://github.com/ifryed/HyperRes
공식 코드https://github.com/it-hao/ADFNet
공식 코드https://github.com/ivrl/ccid
공식 코드Image Denoising with Control over Deep Network Hallucination
https://github.com/j-fhu/cem
공식 코드Interpreting Low-level Vision Models with Causal Effect Maps
https://github.com/jarchv/solverip
공식 코드Generative Flows as a General Purpose Solution for Inverse Problems
https://github.com/jason011212/star-net
공식 코드STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising
https://github.com/jdg900/uid-fdk
공식 코드Unsupervised Image Denoising with Frequency Domain Knowledge
https://github.com/jiahongz/cdn
공식 코드Considering Image Information and Self-similarity: A Compositional Denoising Network
https://github.com/jiahongz/mhcnn
공식 코드A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.