Focused-Region Segmentation for Refocusing Images from Light Fields

Abstract

Since the focused regions of the light-field refocusing image contain depth cues, focused-region segmentation becomes a fundamental step of depth estimation, 3D measurement, and visual measurement. However, in the emerging field of light-field image processing, recent research has emphasized focus evaluation rather than a systematic method for focused-region segmentation. The segmentation algorithms for low depth-of-field images are of significance for this problem, but those algorithms have high time complexity and are not suitable for the computationally intensive applications of light-field imaging. Therefore, based on the pulse synchronous mechanism of the pulse coupled neural network (PCNN), we establish the model of neural firing sequence and some criteria of pixel classification. Further, we design an algorithm of the focused-region segmentation and its parameter settings. The experimental results show that the proposed method segments the refocusing images faster than alternative methods and meets the needs of light-field image processing and related applications.