Adaptive Weighted Guided Image Filtering for Depth Enhancement in Shape-From-Focus

TL;DR

This paper introduces an adaptive weighted guided image filtering method to improve depth map quality in shape-from-focus by preserving edges, details, and reducing noise.

Contribution

It proposes a novel depth enhancement algorithm using AWGIF to decompose and refine depth maps, effectively preserving edges and suppressing noise.

Findings

Superior noise suppression compared to existing methods

Enhanced preservation of depth edges and fine details

Effective on both real and synthetic data

Abstract

Existing shape from focus (SFF) techniques cannot preserve depth edges and fine structural details from a sequence of multi-focus images. Moreover, noise in the sequence of multi-focus images affects the accuracy of the depth map. In this paper, a novel depth enhancement algorithm for the SFF based on an adaptive weighted guided image filtering (AWGIF) is proposed to address the above issues. The AWGIF is applied to decompose an initial depth map which is estimated by the traditional SFF into a base layer and a detail layer. In order to preserve the edges accurately in the refined depth map, the guidance image is constructed from the multi-focus image sequence, and the coefficient of the AWGIF is utilized to suppress the noise while enhancing the fine depth details. Experiments on real and synthetic objects demonstrate the superiority of the proposed algorithm in terms of anti-noise, and the ability to preserve depth edges and fine structural details compared to existing methods.