Construction of all-in-focus images assisted by depth sensing

TL;DR

This paper introduces a depth sensing-assisted multi-focus image fusion method that leverages depth maps to improve focus accuracy and image quality, outperforming existing algorithms in speed and effectiveness.

Contribution

A novel multi-focus image fusion approach using depth sensors and graph-based segmentation to enhance focus detection and image quality.

Findings

Outperforms state-of-the-art methods in speed and quality

Uses depth segmentation to guide focus selection

Produces high-quality all-in-focus images

Abstract

Multi-focus image fusion is a technique for obtaining an all-in-focus image in which all objects are in focus to extend the limited depth of field (DoF) of an imaging system. Different from traditional RGB-based methods, this paper presents a new multi-focus image fusion method assisted by depth sensing. In this work, a depth sensor is used together with a color camera to capture images of a scene. A graph-based segmentation algorithm is used to segment the depth map from the depth sensor, and the segmented regions are used to guide a focus algorithm to locate in-focus image blocks from among multi-focus source images to construct the reference all-in-focus image. Five test scenes and six evaluation metrics were used to compare the proposed method and representative state-of-the-art algorithms. Experimental results quantitatively demonstrate that this method outperforms existing methods in both speed and quality (in terms of comprehensive fusion metrics). The generated images can potentially be used as reference all-in-focus images.