Video denoising with adaptive temporal averaging



video denoising, temporal averaging, averaging interval, edge maps, pixel-domain method, additive white Gaussian noise


Recently the proliferation of digital videos has increased exponentially due to availability of consumer cameras . Despite the improvement in the sensor technologies, one of the fundamental problems is that of noise affecting the video scenes. Recently, adaptive, pixel-wise, temporal averaging methods can advocate in denoising videos. In this work, we adapt the edge maps of frames within temporal averaging to guide the denoising away from the edges. This allows the filtering to remove noise in intermediate flat regions while respecting boundaries of objects better. The experimental results indicate that we can obtain improved video denoising results in comparison to other filtering methods.

Author Biography

V. B. Surya Prasath, University of Missouri-Columbia

Surya Prasath is an assistant professor (research) in the Computer Science Department at the University of Missouri, USA. He received his PhD in Mathematics from the Indian Institute of Technology Madras, India in 2009 (defended in March 2010). He has been a postdoctoral fellow at the Department of Mathematics, University of Coimbra, Portugal, for two years. Since 2012 he is with the Computational Imaging and VisAnalysis (CiVA) Lab at the University of Missouri, USA working on various mathematical image processing and computer vision problems. He had summer fellowships/visits at Kitware Inc. NY, USA, The Fields Institute, Canada, and IPAM, University of California Los Angeles (UCLA), USA. His main research interests include nonlinear PDEs, regularization methods, inverse & ill-posed problems, variational, PDE based image processing, and computer vision with applications in remote sensing, medical imaging domains.