Abstract
As a new information acquisition technology, Wireless Multimedia Sensor Networks (WMSN) usually focused on the acquisition and processing of audio, video and so on. However, video image as one kind of WMSN main sensory information, it includes rich structure, texture and other details, the traditional video image reconstruction algorithm did not make full use of structural information between the pixel neighborhood and self-similarity of image sub-block. Aim at solving this problem, this paper puts forward a novel WMSN video image reconstruction model, which mainly includes three aspects. First, in the framework of Compressed Sensing (CS), an adaptive Block Compressed Sensing (BCS) based on the energy is proposed, image blocks with varying structures is assigned sampling rate adaptively; second, based on structural sparsity and non-local self-similarity of image blocks, establishing structural sparsity regularization and non-local regularization objective function; flnally, Iterative Shrinkage Thresholding (IST) algorithm is introduced to solve the model. Numerical experiments show that our method is e‐cient for WMSN video image recovery, especially preserving the global details of the original video image.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
