Title: Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation,
Abstract: Although block compressive sensing (BCS) makes it tractable to sense
large-sized images and video, its recovery performance has yet to be
significantly improved because its recovered images or video usually suffer
from blurred edges, loss of details, and high-frequency oscillatory artifacts,
especially at a low subrate. This paper addresses these problems by designing a
modified total variation technique that employs multi-block gradient
processing, a denoised Lagrangian multiplier, and patch-based sparse
representation. In the case of video, the proposed recovery method is able to
exploit both spatial and temporal similarities. Simulation results confirm the
improved performance of the proposed method for compressive sensing of images
and video in terms of both objective and subjective qualities.