Title :
Nonlinear image reconstruction in block-based compressive imaging
Author :
Ke, Jun ; Lam, Edmund Y.
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China
Abstract :
A block-based compressive imaging (BCI) system with sequential architecture is presented in this paper. Feature measurements are collected using the principal component analysis (PCA) projection vectors. Then, we discuss an object prior learning framework based on the Field-of-Expert (FoE) model, and provide its implementation in the BCI reconstruction problem. Experimental results are used to demonstrate the reconstruction performance of the FoE-based method.
Keywords :
compressed sensing; data compression; image coding; image reconstruction; learning (artificial intelligence); principal component analysis; BCI reconstruction problem; BCI system; FoE model; PCA projection vector; block-based compressive imaging; feature measurement; field-of-expert model; nonlinear image reconstruction; object prior learning framework; principal component analysis; reconstruction performance; sequential architecture; Detectors; Holography; Image coding; Image reconstruction; Noise measurement; Principal component analysis;
Conference_Titel :
Circuits and Systems (ISCAS), 2012 IEEE International Symposium on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-0218-0
DOI :
10.1109/ISCAS.2012.6271926
