Visually weighted compressive sensing: measurement and reconstruction
Related Experiment Video
Updated: May 16, 2026

Lensless Fluorescent Microscopy on a Chip
Published on: August 17, 2011
Hyungkeuk Lee1, Heeseok Oh, Sanghoon Lee
1Wireless Network Laboratory, Yonsei University, Seoul, Korea. punktank@yonsei.ac.kr
Abstract:
Compressive sensing (CS) makes it possible to more naturally create compact representations of data with respect to a desired data rate. Through wavelet decomposition, smooth and piecewise smooth signals can be represented as sparse and compressible coefficients. These coefficients can then be effectively compressed via the CS. Since a wavelet transform divides image information into layered blockwise wavelet coefficients over spatial and frequency domains, visual improvement can be attained by an appropriate perceptually weighted CS scheme. We introduce such a method in this paper and compare it with the conventional CS. The resulting visual CS model is shown to deliver improved visual reconstructions.
More Related Videos
Related Concept Videos
Computed Tomography
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Reconstruction of Signal using Interpolation
Imaging Studies III: Computed Tomography
Imaging Studies II: Ultrasonography
Measurements of Strain
Imaging Studies for Cardiovascular System IV: CMRI

