A Multihypothesis Based Reconstruction Algorithm for Block Compressive Sensed WVSN Data

Wireless Visual Sensor Networks (WVSNs) have emerged as a prominent technological solution for surveillance and monitoring applications. These networks consist of smart camera sensor nodes, which intermittently capture video frames. These frames are then transmitted to a designated central node known as a sink. Block compressive sensing (BCS) for video compression is a research area that focuses on developing efficient algorithms for compressing video data by exploiting the sparsity of video signals in spatial and temporal domains. The primary goal of BCS-based techniques is to reduce the storage and bandwidth requirements of video data while maintaining acceptable video quality. The associated lightweight encoders and the demand for very less storage space make them suitable for WVSN applications where energy, bandwidth, and storage resources are limited. Reconstruction algorithms play a vital role in maintaining image quality and a prominent one such algorithm is Smooth Projected Landweber (SPL). This paper evaluates the appropriateness of an iterative multihypothesis SPL-based reconstruction strategy in WVSN data aggregation.