| dc.description.abstract | With the rapid evolution of technology and science, many high-resolution optical microscopes (OMs) have been developed by scientists to let researchers be capable of observing smaller and smaller samples. However, the OM’s depth of field (DOF) is limited leading to only partial areas of the captured two-dimensional (2D) images being clear, resulting in information and quality from 2D images being compromised, creating imprecise three-dimensional (3D) images, and difficulties in performing image stitching. Moreover, a high-resolution sample 2D image captured by the OM usually cannot cover the entire measured sample. Also, compared to 3D image microscopic systems, 2D OM image systems cannot allow researchers to understand and analyze the height, shape, and surface profile of the samples. Therefore, developing a novel 3D image algorithm to address the OM’s limited DOF problem, execute image stitching, and produce 3D images is urgently required.
When a high-resolution 2D image is taken by an OM, the observable area inevitably needs to be sacrificed; hence, the entire measured sample is usually not included. Thus, the piezoelectric stage is designed to move the measured sample, which helps increase OM measurement performance. Unfortunately, the piezoelectric stage has unavoidable nonlinear characteristics that will impact the controller’s tracking performance. Consequently, designing an advanced controller to address the piezoelectric stage’s nonlinear characteristics is essential. In a nutshell, it is imperative to establish a 3D optical microscopic imaging (3DOMI) system with a novel 3D image algorithm and an advanced controller.
In this thesis, the proposed 3DOMI system is classified into measurement and scanning parts, including the 3D image algorithm and PSO-BPNN-PID controller. The 3D image algorithm is composed of an image pyramid transform (IPT)-based fusion method, image stitching, and a 2D to 3D conversion approach. Specifically, the novel image fusion rule contains the maximum local area energy method (MLAEM) and pulse-coupled neural network (PCNN) model applied to the IPT-based fusion method. The PSO-BPNN-PID controller combines particle swarm optimization (PSO), back propagation neural network (BPNN), and proportional-integral-derivative (PID) algorithms. The target is to generate extensive and highly precise sample 3D images through the proposed 3DOMI system. Comparing the proposed control method with various traditional control schemes, we used simulations and experimental results to demonstrate that the proposed controller is effective, superior, and more suitable for the proposed 3DOMI system. Furthermore, a known standard sample will be used to calibrate and quantify the measurement capability of the proposed 3DOMI system. In particular, subjective and objective evaluation methods confirm that the fused images obtained by the novel image fusion rules outperform other classical image fusion methods. Ultimately, the proposed 3DOMI system is applied to measure an unknown microelectromechanical systems (MEMS) structure sample, which also performs excellently. | en_US |