Papers with Code 아카이브
Image Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/pputzky/invertible_rim
https://github.com/prakashSidd18/blind_augmentation
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/rami0205/ngramswin
https://github.com/rami0205/ramit
Exploration of Lightweight Single Image Denoising with Transformers and Truly Fair Training
https://github.com/ramsesproject/TomoGAN
https://github.com/rezafuru/generalized-octave-convolution-compression
Generalized Octave Convolutions for Learned Multi-Frequency Image Compression
https://github.com/rflepp/efficient_mobile_denoising_models
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
https://github.com/rish-av/hsid-cnn
https://github.com/rshnn/xray-denoising
Training Deep Learning Based Denoisers without Ground Truth Data
https://github.com/rsin46/deep-image-prior-keras
https://github.com/saeed-anwar/RIDNet
https://github.com/safwankdb/Deep-Image-Prior
https://github.com/sami-automatic/SADNet_Replication
https://github.com/samyakjain0112/Image-Denoising-in-extreme-low-light
https://github.com/sayakpaul/maxim-tf
https://github.com/sayannath/Low-Light-Image-Enhancement
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/sayannath/MIRNet-Flutter
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/sdoria/SID
https://github.com/secml-lab-vt/EvolvingThreat-DeepfakeImageDetect
MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Network
https://github.com/setsunil/dsdnet
Improving Image Restoration by Revisiting Global Information Aggregation
https://github.com/shangqigao/RONet
Rank-One Network: An Effective Framework for Image Restoration
https://github.com/shaonianruntu/MWCNN
https://github.com/sharonwang1/seg_denoising
Segmentation-Aware Image Denoising without Knowing True Segmentation
https://github.com/shibuiwilliam/DeepLearningDenoise
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/skchen1993/SwinIR
https://github.com/sldyns/DnCNN_paddle
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/sldyns/MIRNet_paddle
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/someshfengde/learning_to_see_in_dark
https://github.com/sotiraslab/AgileFormer
https://github.com/sssohrab/sparsifying_groups_imAmbiguation
Learning to compress and search visual data in large-scale systems
https://github.com/subhayanmukherjee/cnninsar
https://github.com/sunilbelde/Imagedenoising-dncnn-keras
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/sunilbelde/Imagedenoising-dncnn-ridnet-keras
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/taowangzj/llformer
https://github.com/teakkkz/imageSR
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.