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High dynamic range imaging for dynamic scenes via locality-constrained low-rank matrix completion
Author(s): Hai Zhang; Mali Yu
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Paper Abstract

High dynamic range (HDR) imaging expands the capabilities of a camera by synthesizing a sequence of different exposure images. However, due to camera and object motion, ghosts exist in the synthesized HDR image. The low-rank matrix completion (LRMC) model has achieved some success in ghost-free HDR imaging, but leads to artifacts around the observation region edges for neglecting local image structure. In this paper, a locality-constrained LRMC (LcLRMC) model is proposed, in which we iteratively update the background irradiance and the observation region based on the result from previous iteration. Specifically, the proposed method incorporates global and local structures. Experimental results show that compared to the conventional LRMC model, the proposed method effectively eliminates artifacts around the observation region edges.

Paper Details

Date Published: 3 January 2020
PDF: 8 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113732H (3 January 2020); doi: 10.1117/12.2557708
Show Author Affiliations
Hai Zhang, Jiujiang Univ. (China)
Mali Yu, Jiujiang Univ. (China)

Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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