Intelligent microscopic 3D shape reconstruction method based on 3D time-frequency transformation

Abstract

Three-dimensional (3D) shape reconstruction methods based on image focus information generally use a unified focus evaluation metric for the depth-of-field image sequences of microscopic objects. With the absence of the correlation among these sequences, this kind of reconstruction method fails to correct the depth errors of consecutive frame images that result from textural sparsity or low contrast in images. In this paper, different image sequences of microscopic objects are interpreted as 3D image data, such that the correlation among all the sequences can be introduced to the reconstruction. Then, a reconstruction framework encompassing multiview analysis, locally stable clustering, and selective fusion as its key components is established from the perspective of 3D time-frequency transformation. To be specific, we analyze the advantage of 3D image data in shape reconstruction over general 2D image data and construct a 3D time-frequency transform that maps a 3D image to depth images, which contain information of varying scales, regions, and directions. Next, to enhance depth image features, a locally stable clustering algorithm is constructed based on multimodal textural features, which improves the adaptive selection ability of depth images with good homogeneity. Finally, we propose the selective fusion of depth images, which outputs accurate 3D morphology results by fitting multiple layers of noise-free depth images through a layer-by-layer sifting balance tree designed in this study. The effectiveness of the proposed method is verified on simulation data and real scenes. The reconstruction method of this perspective may bring fresh air to the field of submicron industrial measurement and sophisticated manufacturing.