Recently, the low-rank and sparse decomposition

problem has attracted attention in several applications, especially
surveillance videos. Due to the physical limitations in acquisition
systems, measured frames are blurred by a low-pass filter.

In this article, we aim to decompose blurred videos' frames

into low-rank and sparse components, in order to extract the

background. Unlike conventional methods, we simultaneously take into
account the blurring effect, as well as the missing data. Our simulation
results confirmed the advantage of this approach in extracting low-rank
components in surveillance videos.