DVDnet swMATH ID: 44025 Software Authors: Tassano, Matias; Delon, Julie; Veit, Thomas Description: DVDnet: A Fast Network for Deep Video Denoising. In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot compete with the performance of patch-based methods. However, our approach outperforms other patch-based competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as a small memory footprint, and the ability to handle a wide range of noise levels with a single network model. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics. The experiments show that our algorithm compares favorably to other state-of-art methods. Video examples, code and models are publicly available at https://github.com/m-tassano/dvdnet Homepage: https://arxiv.org/abs/1906.11890 Source Code: https://github.com/m-tassano/dvdnet Dependencies: Python Keywords: Computer Vision; Pattern Recognition; arXiv_cs.CV; Image and Video Processing; arXiv_eess.IV Related Software: FastDVDnet; ImageNet; DnCNN; FFDNet; MemNet; PyTorch; ViDeNN; PatchMatch; Swin Transformer; Caffe; FOCNet; BSDS; U-Net; OpenGL; Pfinder; MOTS; StyleGAN2; ResMLP; BRISK; EfficientDet Cited in: 3 Documents all top 5 Cited by 10 Authors 1 Elad, Michael 1 Fei, Lunke 1 Kawar, Bahjat 1 Lin, Chia-Wen 1 Szeliski, Richard 1 Tian, Chunwei 1 Vaksman, Gregory 1 Xu, Yong 1 Zheng, Wenxian 1 Zuo, Wangmeng Cited in 3 Serials 1 Neural Networks 1 SIAM Journal on Imaging Sciences 1 Texts in Computer Science Cited in 2 Fields 3 Computer science (68-XX) 2 Information and communication theory, circuits (94-XX) Citations by Year