| Summary: | 博士 === 國立中央大學 === 光電科學與工程學系 === 102 === This thesis introduced the principle of the digital holographic data storage system (DHDSS), and proposed the alignment method and recognition method for improving the data channel in the DHDSS. There are two alignment methods proposed and compared in this thesis, one is gray level weighting method, GLWM, and another one is alignment method based on structure similarity (SSIM), AMBS. Also, the recognition method is developed by using SSIM, called recognition method based on SSIM (RMBS). Experiments are performed on three cases that demonstrate the effectiveness of these methods.
GLWM is the first method that is proposed herein to improve the alignment in the DHDSS. It uses a gray-scale, which is a characteristic of the data channel in the DHDSS, to locate accurately the fiducial point in a checkerboard image. However, an issue associated with boundary identification affects the location of the fiducial point. AMBS corrects a fault in the boundary identification, making it a more accurate alignment method to yield the accurate fiducial points in the data pages via an image quality assessment, SSIM. Since AMBS provides accurate alignment, the DHDSS has a 1.5 dB decrease in bit error rate (BER) and a greater tolerance for shift during the data readout. Also, inexpensive optical elements can be used in the DHDSS to reduce system cost while maintaining a sufficient BER.
RMBS is like AMBS in that its performance is improved by SSIM. RMBS uses an image database to store several noise-free images that are utilized in the recognition procedure. RMBS attempts to replace the thresholding method in the data channel by the effective SSIM. Unfortunately, the performance of RMBS in retrieving data from the data pages in Shifting case should be further improving because the noise-free images in the database do not unequivocally recognize the target image with serious distortion as a corresponding reference image. Nevertheless, RMBS still outperforms the thresholding method in some shifting cases.
Combining AMBS and RMBS can cut the system costs of the DHDSS, while keeping it error-free. The authors hope that the combination of the two methods will grow DHDSS into a finished commercial product.
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