| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 92 === Recently, many researches have been proved that a programmable method can perform most of complicated image process tasks under the architecture of Cellular Neural Network (CNN). Real-time and parallel analog computing elements are contained in the architecture of CNN. There is an ideal characteristic that each computing unit is regular two-dimensional array and connects with its neighborhood locally. Because of this characteristic, the architecture of CNN is easy for VLSI implementation. There are three parts in the thesis: (1) image stabilization technique, (2) CNN-based image stabilization technique and (3) its analog circuit design by (2). Image stabilization technique contains two main blocks. One is the computation of the motion vectors caused by vibration and the other is in compensation for the motion vector. In the thesis, in order to enhance the ability of real-time processing, the algorithm is designed to be the CNN-based architecture and with the implementation of parallel and real-time analog circuit.
We aim at the property of the proposed algorithm to design application-driven CNN circuit for the image stabilizer. The CNN circuit is a multi-layer structure and its template is 3�~3. The size of CNN array is 1/120 of an image. Current mirror is used to reduce complexity and to extend positive and negative current for weighting of each cell. Current signals are easily combined in the same node as well. Due to parallel processing and local connectivity, CNN is suitable for implementation of standard mixed-signal CNOS process.
Finally, the simulation results of MATLAB and HSPICE show that the proposed CNN-based image stabilization technique has the fast and real-time processing ability in image compensation.
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