https://github.com/mdipcit/GTCNN
Gated Texture CNN for Efficient and Configurable Image Denoising
Papers with Code 아카이브
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mechatoz/convolt
Convolutional Dictionary Learning: Acceleration and Convergence
https://github.com/megengine/nbnet
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
https://github.com/megvii-research/tlsc
https://github.com/meitalB/NN
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/melobron/Noisier2Noise
https://github.com/merria28/NTIRE_GRDN
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling
https://github.com/mindspore-ai/models/blob/master/research/cv/LearningToSeeInTheDark
https://github.com/mindspore-ai/models/tree/master/official/cv/dncnn
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/mindspore-ai/models/tree/master/official/cv/unet
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
https://github.com/mindspore-ai/models/tree/master/research/cv/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/mingcv/ytmt-strategy
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/miriskalt/AnomalyDetectionAutoencoder
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/mirthai/med3dvlm
Geodesic Diffusion Models for Medical Image-to-Image Generation
https://github.com/mq0829/DL-CACTI
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/murufeng/FUIR
https://github.com/mv-lab/swin2sr
https://github.com/neeraj3029/Ne2Ne-Image-Denoising
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
https://github.com/nekitmm/starnet
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/nickolor/Learning-to-See-in-the-Dark
https://github.com/nickolor/Learning-to-See-in-the-Dark_and_DeepISP
https://github.com/nunof98/AiVision_Anomaly_Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/olaviinha/NeuralImageSuperResolution
https://github.com/opendenoising/opendenoising-benchmark
OpenDenoising: an Extensible Benchmark for Building Comparative Studies of Image Denoisers
https://github.com/optml-group/red-adv
Reverse Engineering of Imperceptible Adversarial Image Perturbations
https://github.com/pilot7747/sldl
https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/pminhtam/DBSN_pytorch
https://github.com/pminhtam/DeepBurstDenoising
https://github.com/pminhtam/KPN_attention
Multi-Kernel Prediction Networks for Denoising of Burst Images
https://github.com/pminhtam/KPN_tf
Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst Images
https://github.com/pminhtam/MIRnet_SIDD
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/pminhtam/MWCNN
https://github.com/pminhtam/NBNet
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
https://github.com/pminhtam/Neigh2Neigh
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
https://github.com/pminhtam/PT-MWRN
Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.