A 3D optical microscopic imaging system based on PSO-BPNN-PID control and image pyramid transform fusion with experimental verification

Abstract The limited depth of field of optical microscopes typically results in the blurring of all but a small portion of the image, making it difficult to assess the height, shape, and surface profile of samples. This issue can be partly resolved by capturing a series of images at various sample distances by adjusting the piezoelectric stage; however, scanning performance is prone to perturbations resulting from nonlinear piezoelectric response characteristics. This paper presents a novel three-dimensional optical microscopic imaging system that uses a particle swarm optimization control scheme in parallel with a back-propagation neural network to achieve high-precision trajectory tracking. The system also features an algorithm to perform image pyramid transform fusion, image stitching, and 2D-to-3D conversion to enable the capture of 3D surface profiles of high accuracy. In experiments, the proposed scheme significantly outperformed existing methods in terms of accuracy and the level of detail.