3D shape reconstruction from multifocus image fusion using a multidirectional modified Laplacian operator

• Published in: Pattern recognition 2020-02, Vol.98, p.107065, Article 107065
• Main Authors: Yan, Tao, Hu, Zhiguo, Qian, Yuhua, Qiao, Zhiwei, Zhang, Linyuan
• Format: Article
• Language: English
• Subjects: 3D shape reconstruction; Image fusion; Microscopic imaging; Nonsubsampled shearlet transform; Shape-from-focus
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2019.107065

•The feasibility of combining multifocus image fusion and shape-from-focus methods to obtain real 3D reconstruction results is introduced.•The advantages of 3D shape reconstruction using nonsubsampled shearlet transform and a multidirectional modified Laplacian are discussed.•The major challenges faced by conventional shape-from-focus algorithms in reconstruction of low-contrast regions are analyzed.•The difficulties of existing image fusion methods in mining depth information are summarized.•Future applications of the proposed 3D shape reconstructions in the field of intaglio printing are presented. Multifocus image fusion techniques primarily emphasize human vision and machine perception to evaluate an image, which often ignore depth information contained in the focus regions. In this paper, a novel 3D shape reconstruction algorithm based on nonsubsampled shearlet transform (NSST) microscopic multifocus image fusion method is proposed to mine 3D depth information from the fusion process. The shift-invariant property of NSST guarantees the spatial corresponding relationship between the image sequence and its high-frequency subbands. Since the high-frequency components of an image represent the focus level of the image, a new multidirectional modified Laplacian (MDML) as the focus measure maps the high-frequency subbands to images of various levels of depth. Next, the initial 3D reconstruction result is obtained by using an optimal level selection strategy based on the summation of the multiscale Laplace responses to exploit these depth maps. Finally, an iterative edge repair method is implemented to refine the reconstruction result. The experimental results show that the proposed method has better performance, especially when the source images have low-contrast regions.