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.

Scanless three-dimensional (3D) imaging technology has received extensive attention in recent years due to its rapid detection and system reliability. Compressed sensing imaging technology provides a new solution for the realization of scan-free 3D imaging. In this paper, a 3D imaging method based on dual-frequency laser phase ranging based on compressed sensing technology is introduced and realized. Using the combination of dual-frequency laser phase ranging and compressed sensing theory, two-dimensional range reconstruction from the time-domain light intensity signal collected by a single-point detector is performed. Aiming at the spatial sparsity of the target scene, this technology uses the compressed sensing algorithm to solve the phase information of the two-dimensional spatial distribution contained in the time domain signal so as to invert the 3D image information of the target scene and realize the effect of scanning-free 3D imaging. First, the feasibility of the system is verified by simulations, and the imaging effects of different reconstruction algorithms on different terrains are compared. Second, a non-scanning 3D imaging experimental platform is designed and built. Finally, the 3D images of multiple objects with 32 × 32 resolution are successfully reconstructed through experiments with a compression ratio of 0.25. The ranging accuracy of this system is 0.05 m. This work is promising for applications in multiple objects’ fast detections.