Research On Optical Wavelet Parallel Processing

The fundamental theory of optical wavelet parallel processing and its implementations are systematically described in this dissertation. The techniques for realization of optical wavelet parallel processing are analyzed in detail. The applications in feature extraction, texture segmentation and pattern recognition are explored.The basic ideas and methods to realize optical wavelet parallel processing, including combination of array elements, spatial division multiplexing and balance between input image's scale-rotation and multiple wedge-ring filters, are proposed. The key problem of the implementations is to display the 4-D outputs of optical wavelet transform. A micro-optical wedge-ring wavelet detector is constructed by the means of spatial division multiplexing in the frequency domain. A binary optical element with functions of multiple wavelet-filtering, beam splitting and focusing is designed and fabricated. The simulated and experimental results testify the validity in feature extraction with the element. Two improved schemes, redistribution of the outputs and adding a Dammann grating to release the burden of the element, are brought forward to overcome the zero grade spectrum.Diffraction properties of conventional correlation and wavelet correlation based on the volume holographic storage are simulated perfectly for the first time. The modulation of sine function and the cross-talk noise on two different recording directions are studied. The cross-talk noise is decreased evidently with the join of wavelet transform in the volume holographic correlation system. The correlation outputs are extended from one dimension to two dimensions. Three combination forms of volume holographic storage and wavelet transform are proposed and compared. A volume holographic wavelet correlation system in a photorefractive crystal based on the combination form named "wavelet extracted features read out wavelet extracted features" is constructed, which has the minimal cross-talk noise. Experiments are performed to validate the importance of wavelet transform for the improvement of the correlation quality and the recognition accuracy.Several important issues of the volume holographic wavelet correlation system in a photorefractive crystal are discussed specially to improve the performance of the system. These issues are parallelism, multichannel development, choice of wavelet parameters and recognition invariance. An estimation method and some enhancementapproaches of the parallelism are proposed. A neural network is designed to optimize parameters of the wavelet filters. Two novel systems to implement the parallel processing of multiple input-image-channels and multiple wavelet-filter-channels are constructed. The invariance of the system is studied by simulation and experiments. It is concluded that the shift invariance can be improved with a bigger focal length of the transform lens, and a scheme with the rotation invariance at any rotation angle is proposed and realized.Post-processing methods of the optical outputs are studied thoroughly. A fuzzy c- means clustering algorithm combined with the simulated annealing mechanism is developed for texture segmentation. Different integration forms of the extracted features are used to improve the scale and rotation invariance. A fuzzy synthesis judgement algorithm and a fuzzy clustering recognition algorithm are proposed to improve the recognition performance of the system.The applications of the proposed systems in texture segmentation, associative searches, human identification and car plate recognition are performed by experiments.