Summary
International Technical Conference on Circuits/Systems, Computers and Communications
2016
Session Number:P1
Session:
Number:P1-17
Performance Analysis of Compressive Image Sensing for Various Spatial Resolutions based on Sparsity Measure
Moonhee Kim, Younghyeon Park, Byeungwoo Jeon ,
pp.895-898
Publication Date:2016/7/10
Online ISSN:2188-5079
Summary:
Compressive sensing (CS) can lead sampling with a lower rate than that suggested by the Nyquist/Shannon's theorem for the sparse signals. The block-based CS (BCS) requires less memory size for the sensing matrix than the frame-based one. Image characteristic can vary according to various spatial resolution of image capturing device, so it is expected to affect the quality of the reconstructed images from compressesively smapled data. We compare objective qualities of reconstructed images of BCS for various image spatial resolutions using Gini index as sparsity measure. The simulation result shows that reconstructed image has better objective quality by increasing spatial resolution.