Research on Deblurring of Motion Blurred Image

Ting Lu

School of Finance, Anhui University of Finance and Economics, Bengbu, 233030, China

Abstract: In the era of big data, we are exposed to more and more digital images, and inevitably, various degrees of deterioration and distortion occur in the process of image formation, transmission, storage, recording and display. People's lives are becoming more and more abundant, the application of cameras is becoming more and more common, and motion blur is also a problem that is easy to occur in the imaging process. As images become more closely related to people's lives, the demand for high quality images is increasing. In this paper, we first introduce two methods for the formation of motion blur images, namely the heavy-tailed distribution method and the Fourier transform method. The heavy-tailed distribution method can determine whether the image is a blurred image by using the obtained graphic, and the Fourier transform method is mainly used to determine which kind of blurred image the fuzzy image is specifically. Through the analysis of the causes of image formation, we have stepwise optimization of our images from two different angles, namely, deblurring and denoising. We first assume that the noise conditions are known, and use the modeling method to simulate the fuzzy trajectory of the motion blur picture, and obtain a clear image based on the obtained fuzzy trajectory combined with the non-blind deblurring algorithm.

Keywords: Motion blurred image defuzzification; Fourier transform; Normalized factor model; Non-blind deblurring algorithm; Wiener filtering