Estimating Parameters for Deblurring in Two-Dimensional Linear Motion

Abstract

The technology in the field of multimedia image processing improves every day, but there are still some problems that deserve to be further explored and improved. People like to take and preserve the impressive scenery as an unforgettable memory. However, if the object is moving or the photographer is shaking, the captured image is easily blurred, and this blur is called motion blur. However, deblurring an image without the information of speed and direction of moving objects is still a well-known ill-posed problem. In this paper, we proposed a system to deblur image that can estimate important parameter advance to reduce the complexity of deblurring process. The data of sensor of moving object is collected. The BPN neural network is used to train to classify the speed and direction of the object from the sensor data. After that, we can estimate the speed and direction of objects without other algorithms. With such important parameters, deblurring processing will more efficient.