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

arXiv: https://arxiv.org/abs/2201.06823 · HTML: https://mlsanity.com/paper/2201.06823/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [Variational Depth from Focus Reconstruction](https://mlsanity.com/paper/1408.0173/) (2014)
- [Dual-mode Dynamic Window Approach to Robot Navigation with Convergence Guarantees](https://mlsanity.com/paper/1808.05869/) (2018)

## Built on by (most cited first, 2 of 2)

- [Enhanced Edge-Perceptual Guided Image Filtering](https://mlsanity.com/paper/2310.10387/) (2023)
- [Gaussian highpass guided image filtering](https://mlsanity.com/paper/2503.03284/) (2025)

## Abstract (arXiv)

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.