Exploiting Temporal Attention Features for Effective Denoising in Videos

5 August 2020

Papers citing "Exploiting Temporal Attention Features for Effective Denoising in Videos"

8 / 8 papers shown

Title
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation Matias Tassano J. Delon T. Veit 68 238 0 01 Jul 2019
ViDeNN: Deep Blind Video Denoising M. Claus Jan van Gemert 59 96 0 24 Apr 2019
Model-blind Video Denoising Via Frame-to-frame Training T. Ehret Axel Davy Jean-Michel Morel Gabriele Facciolo Pablo Arias 98 82 0 30 Nov 2018
Learning to See in the Dark Cheng Chen Qifeng Chen Jia Xu V. Koltun 286 1,185 0 04 May 2018
Burst Denoising with Kernel Prediction Networks B. Mildenhall Jonathan T. Barron Jiawen Chen Dillon Sharlet Ren Ng Robert E. Carroll 105 395 0 06 Dec 2017
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising Peng Sun W. Zuo Lei Zhang 125 2,123 0 11 Oct 2017
On Loss Functions for Deep Neural Networks in Classification Katarzyna Janocha Wojciech M. Czarnecki UQCV 72 551 0 18 Feb 2017
Empirical Evaluation of Rectified Activations in Convolutional Network Bing Xu Naiyan Wang Tianqi Chen Mu Li 135 2,912 0 05 May 2015