High energy flash X‐ray image restoration using region extrema and kernel optimization

Autor: Xiaolin Wang, Qingwu Li, Jinxin Xu
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: IET Image Processing, Vol 15, Iss 12, Pp 2970-2985 (2021)
Druh dokumentu: article
ISSN: 1751-9667
1751-9659
DOI: 10.1049/ipr2.12284
Popis: Abstract The quality of high energy flash X‐ray images is crucial to the high‐precision diagnosis of object density. High energy flash X‐ray radiography is susceptible to the system blur, which usually causes the poor quality of static images. In response to this, a novel restoration algorithm using region extrema and kernel optimization (REKO) is presented. Based on the observation that the region extrema distribution of blurred high energy flash X‐ray images deviates from opposite ends of image grey domain, the sparseness‐inducing prior for regularizing image region extrema is applied to construct the restoration model. Considering the sparse characteristics of blur kernels, the sparseness‐inducing regularization is incorporated to constrain blur kernels in the restoration model. The non‐convex and non‐linear objective function is gradually minimized through energy alternating minimization and dually linear approximation. Furthermore, a continuity enforced kernel optimization algorithm is proposed to estimate more accurate blur kernels. The discontinuous kernel elements are suppressed by extracting the main structure of blur kernels and constructing kernel continuity function in cross windows. Experimental results demonstrate that our algorithm can more accurately estimate blur kernels and achieve restoration results with sharper edges on high energy flash X‐ray images.
Databáze: Directory of Open Access Journals
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