Sequence Image Registration for Large Depth of Microscopic Focus Stacking

IEEE Access

Rongsheng Lu¹  Zilong Zhang¹  Ang Wu¹  Yanqiong Shi² 

[1] School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei, China;School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, China;

关键词: Focus stacking;    microscopic measurement;    sequence image registration;    focus segmentation;    scale invariant feature transform;   

DOI  :  10.1109/ACCESS.2019.2963633

来源: DOAJ

【 摘 要 】

The large depth of images for microscopic measurement can be achieved by using focus stacking techniques with a small depth of field of objective lens. It is implemented by fusing the image sequences of short depth images. However, the non-linear movement of the objective imaging system or the measured object caused by the moving stage straightness error brings the misalignment of the image sequences, such as transversal translation, rotation, and tilting. All of these interferences, as well as the image brightness variation must be corrected by image registration before fusing the image sequences. In this paper, a fast-automatic registration method based on the scale invariant feature transform (SIFT) is proposed. It is achieved by firstly segmenting the focal regions of the image sequences through fast edge detection. Then the image features are extracted within the small segmented focal areas. It greatly reduces the computational cost of feature extraction and the following steps of image correction, and alignment. In the process, the random sampling consistency (RANSAC) algorithm is also used to remove the mistake features. The Laplacian pyramid method is adopted for the large depth of image fusion. The experimental results show that the proposed method is more efficient than the traditional SIFT algorithm. Its registration efficiency is improved by about 60%. This method facilitates the high-precision and real-time imaging of a monocular three-dimensional focus stacking.

【 授权许可】

Unknown