Depth Image Enhancement Algorithm Based on Fractional Differentiation

Author:

Huang Tingsheng12ORCID,Wang Xinjian12ORCID,Xie Da12,Wang Chunyang12ORCID,Liu Xuelian2ORCID

Affiliation:

1. School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China

2. Xi’an Key Laboratory of Active Photoelectric Imaging Detection Technology, Xi’an Technological University, Xi’an 710021, China

Abstract

Depth image enhancement techniques can help to improve image quality and facilitate computer vision tasks. Traditional image-enhancement methods, which are typically based on integer-order calculus, cannot exploit the textural information of an image, and their enhancement effect is limited. To solve this problem, fractional differentiation has been introduced as an innovative image-processing tool. It enables the flexible use of local and non-local information by taking into account the continuous changes between orders, thereby improving the enhancement effect. In this study, a fractional differential is applied in depth image enhancement and used to establish a novel algorithm, named the fractional differential-inverse-distance-weighted depth image enhancement method. Experiments are performed to verify the effectiveness and universality of the algorithm, revealing that it can effectively solve edge and hole interference and significantly enhance textural details. The effects of the order of fractional differentiation and number of iterations on the enhancement performance are examined, and the optimal parameters are obtained. The process data of depth image enhancement associated with the optimal number of iterations and fractional order are expected to facilitate depth image enhancement in actual scenarios.

Funder

National Key R&D Program of China

Publisher

MDPI AG

Subject

Statistics and Probability,Statistical and Nonlinear Physics,Analysis

Reference24 articles.

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