Regularized restoration using image fusion for digital auto-focusing

Abstract

Fusion-based image restoration is an effective way to remove multiple out-of-focus blurs in images. Although image restoration and image fusion have been successfully investigated and developed over the years, little effort has been made to combine them. In this paper, we present an integration method of the two approaches and make them benefit from each other to obtain significantly improved performance. Based on the proposed fusion approach, we present a novel digital auto-focusing algorithm, which restores an image with multiple, differently out-of-focused objects. To this end, an out-of-focused image is first restored by using a directionally regularized iterative restoration with multiple regularization parameters. By assembling multiple, restored regions from consecutive levels of iterations, a salient focus measure is formed as a new query using sum modified Laplacian (SML). An auto-focusing error metric (AFEM) is used as an appropriate termination criterion for iterative restoration. A novel soft decision fusion and blending (SDFB) algorithm combines images from restored by different point-spread functions (PSFs) and enables smooth transition across region boundaries for creating the finally restored image using a pseudo activity measure. Experimental results show that the proposed auto-focusing algorithm provides sufficiently high-quality restored images so that it can be used or devices such as a digital camera and a camcorder.