Segmenting a Noisy Low-Depth-of-Field Image Using Adaptive Second-Order Statistics

Abstract

We propose a novel algorithm to segment a low depth-of-field (DOF) image into its focused region-of-interest (ROI) and defocused background using adaptive second-order statistics (ASOS). Most previous methods depend on the assumption that the images are in noise-free conditions, which leads to high false positive rates in noisy images. In this letter, we introduce a novel image segmentation algorithm for noisy low-DOF images. Specifically, we propose a novel feature transform method, called ASOS, which indicates the spatial distribution of the high-frequency components in the face of noisy low-DOF images. Experimental results demonstrate that the proposed method is effective for image segmentation in noisy images compared to several state-of-the-art methods proposed in the literature.