Guided image filtering in shape-from-focus: A comparative analysis

Ali, Usman; Lee, Ik Hyun; Mahmood, Muhammad Tariq

doi:10.1016/j.patcog.2020.107670

Cited by 40 publications

(16 citation statements)

References 20 publications

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“…Gray Level Variance (GLVA) is one of the most popular FM operators, [16], [31], [36], [37]. It follows the assumption that regions with high gray level values are sharper than regions with low gray level values.…”

Section: Related Workmentioning

confidence: 99%

Focus Measurement in Color Space for Shape From Focus Systems

et al. 2021

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Shape from Focus (SFF) has been studied extensively in computer vision for 3D shape and depth recovery. The first stage in SFF methods is to compute the focus value of every pixel by converting the colored images into gray scale and then apply the focus measure operator. Converting colored values in the images into gray scale values may lead to imprecise mapping of pixels with different colored values onto the same gray scale value, this affects the overall accuracy of the system. In a colored image, the focused pixels maintain a considerable color difference from their neighboring pixels as compared to the defocused ones, which are blended into their neighborhood. This article presents an alternative method to measure the degree of focus by directly processing colored images. The color differences of the neighbor pixels with respect to the central pixel are obtained and summed together, this is followed by calculating their spread. The sum and the spread are combined to measure the degree of focus of the pixel in consideration. The proposed focus measure is then used for shape recovery of various simulated and real objects and is compared with previous techniques. The comparison results show the proposed method has the highest correlation and smallest RMSE values confirming the effectiveness of using color images for shape recovery.

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Section: Related Workmentioning

confidence: 99%

Focus Measurement in Color Space for Shape From Focus Systems

et al. 2021

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“…However, these methods like the general depth-related literature, consider to update depth without considering any prior information. Furthermore, following the recent advancements in joint filtering (Ali et al, 2021), the researchers have applied guided (joint) filtering scheme for depth improvement in the SFF.…”

Section: Related Workmentioning

confidence: 99%

Depth Hypotheses Fusion through 3D Weighted Least Squares in Shape from Focus

Ali

Mahmood

2021

Microsc Microanal

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“…Recently, guided filtering algorithms have been applied for SFF to improve depth maps [19,20]. Guided filtering is a technique to obtain the target image with richer details via the structural information of the guided image.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Li²,

Zheng³

et al. 2022

Preprint

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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.

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Guided image filtering in shape-from-focus: A comparative analysis

Cited by 40 publications

References 20 publications

Focus Measurement in Color Space for Shape From Focus Systems

Focus Measurement in Color Space for Shape From Focus Systems

Depth Hypotheses Fusion through 3D Weighted Least Squares in Shape from Focus

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

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