Due to the delay of sequential 3-D Lidar image acquisition while an uncooperative human target is in motion, the image may generate missing or occlusion pixels. We wish to minimize the impact of image acquisition of a moving target for aided target recognition. We apply the standard Fourier transform algorithms for an error resilience restoration to minimize the impact to the Human Visual System (HVS) which tends to overly emphasize the edge and the artificially generated discontinuity in missing pixels. We compared (i) classical phase retrieval scheme: Gerchburg-Saxon-Hayes-Papoulis (GSHP) and (ii) the Compressive Sensing scheme: Candes-Romberg-Donohoe-Tao (CRDT). The following two lessons were learned: The mechanism is based on Gibbs overshooting of a step-discontinuity. It is based on relocating the sparsely sampled zeros at missing pixel locations a la spatial and spatial frequency inner product conformal mapping property.

Date Published: 24 June 2014
PDF: 5 pages
Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 91180K (24 June 2014); doi: 10.1117/12.2054409

Published in SPIE Proceedings Vol. 9118:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
Harold H. Szu; Liyi Dai, Editor(s)