Image Processing Using Hybrid Neural Network

Image processing technology includes image restoration, image compression, image segmentation, image enhancement, etc. Currently, it is widely adopted in and spread to fields such as remote sensing, character recognition, radiographic film and so on. The technology enlarges the utilization of images, which we obtain from the boundless universe. And we can design digital video disc, satellite nephogram cemara or medical supervise system with high-performance based on the image technology.Artificial neural networks technology research has gone a long way in the past decade. The specialists at home and abroad in the image processing field have paid high attention and been engaged in the advantages of Neural Network techniques such as the abilities of parallel computing, nonlinear mapping and self-adaptiveness, and applied a variety of Neural Network models into the image processing field.According to the. practical combination of the neural networks technology and imge processing technology, this thesis researches into new algorithms in detail.The main contents of this thesis are as follows:Firstly, the author gives an overview on the characteristics and history of the image processing technology and image coding technology and introduces some basic coding algorithms in detail. The author also discusses on some problems on application, which will be partly settled in this thesis.And then, the author reviews many applications of Neural Network in image processing and discusses the status quo and prospect of Neural Network. All these applications are categorized according to the phases of image processing and discussed in detail. In the conclusion of paper, he lists several disadvantages of Neural Network techniques.Combining the Self-organizing feature map theory (SOFM) and Principle component analysis theory (PCA), the author proposes an image compressing algorithm based on PCA/SOFM hybrid neural network, which has the advantages of both PCA and SOFM. A new method of selecting initial codebook and distortioncriterion is presented to improve the efficiency of SOFM neural network according to the statistic feature of PCA transformational coefficient. Simulation results show that compared to successive PCA and SOFM algorithm or basic SOFM algorithm, PCA/SOFM hybrid algorithm has many advantages: less memory occupation; substantial reduction in computation and the better performance of codebook.What is more, the author probes into hardware-based neural network, which he has engaged with image compression above.Finally, the author makes a conclusion and proposes the future research directions.