感測器應用於影像去模糊之探討

For the past many years, imaging technology has been flourishing innovation very much. There have developed a lot of multimedia imaging equipment, such as, SLR camera, dynamic sport camera, smartphone photography, vehicle driving record etc. Nowadays, some multimedia imaging equipment is also generally be- gun to be widely used in public. Unfortunately, despite the advances in image processing technology is improved, some of the issues have not been absolutely solved. For example, attempting to take pictures in the high-speed moving situation causes motion blur on the pictures. These potential challenges are worth to be researched and improved. In this thesis, there are two steps for deblurring the images quickly. The first part is clustering the data, and the other part is deblurring the images in details. At first, we would simulate the blurred situation with different acceleration and display deblurring effects on the motion images. During the experiment, we will set up the photographic equipment to record motion blurred images at the different acceleration on sliding track. In order to facilitate analysis of the acceleration value, we utilize the neural network technique to deal for noise reduction on the acceleration data. Then, we also use K-means methodology to cluster the acceleration data into some clusters. Furthermore, we would determine the optimal point spread function in deconvolution operation.