Block compressive sensing of image and video with nonlocal Lagrangian multiplier and patch-based sparse representation
2017 (English)In: Signal processing. Image communication, ISSN 0923-5965, E-ISSN 1879-2677, Vol. 54, p. 93-106Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2017. Vol. 54, p. 93-106
Keywords [en]
Block compressive sensing; Distributed compressive video sensing; Total variation; Nonlocal means filter; Sparsifying transform
National Category
Media Engineering
Identifiers
URN: urn:nbn:se:liu:diva-138280DOI: 10.1016/j.image.2017.02.012ISI: 000401202400009OAI: oai:DiVA.org:liu-138280DiVA, id: diva2:1109085
Note
Funding Agencies|National Research Foundation of Korea (NRF) grant - Korean government (MSIP) [2011-001-7578]; MSIP G-ITRC support program [IITP-2016-R6812-16-0001]; ERC via Grant EU FP 7 - ERC Consolidator Grant [615216 LifeInverse]
2017-06-132017-06-132017-06-28