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

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.