Compressive Sensing through Iterative Reweighted Optimization for Consumer Electronics Applications

Traditional compressive sensing (CS) is related to the assumption that signals which depicts sparse representations which are limited in size. However for many realistic applications in consumer electronics the sparsity-based dictionary prior in home electronics, the sparsity is featured by undefined specifications. Usage of CS techniques to such applications without any modification could result in a deteriorated and underived output. In this paper, we propose a novel adaptive weighted constraint optimization based (AWCO-CS) which can model the unknown parameters with weighted constraints which adapts with each recursion. The proposed (AWCO-CS) algorithm provides superior results compared to its counterparts.