Rapid axial scanning system for acquisition of defocus-enhanced dataset

Abstract

Defocus blur in images is often the result of inadequate camera settings or depth of field restrictions. In recent years, with the emergence and advancement of deep learning, learning representation-based methods have achieved remarkable success in the field of image defocus enhancement. In this paper, a rapid axial scanning system was proposed for efficient acquisition of defocused-enhancement datasets. A multi-focus image sequence with different focus depths of a same scene is captured, and it is utilized to generate a full-focus image (ground truth) through image fusion, to build a set of defocused enhancement datasets. Multiple defocused-enhancement datasets can be obtained based on this approach. Experimental results confirm the feasibility and effectiveness.