Object Motion Deblurring in Single Image Under Static Background

• Main Authors: Tengteng Zhang, Sensen Song, Zhenhong Jia, Jie Yang, Nikola K. Kasabov
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Motion blur; automatic GrabCut segmentation; sharpening enhancement; IRLS algorithm
• Online Access: https://ieeexplore.ieee.org/document/9281343/

When shooting a moving object, as the object moves too fast or the camera's exposure time is too long, smears may occur in the image, which would result in motion blur. The blind restoration of object motion blur is a challenging inversive problem. To effectively extract useful information from blurred images, this paper proposes a new method to remove motion blur, which is based on the maximum a posterior (MAP) framework. Firstly, the framework combines guided filtering and automatic GrabCut image segmentation algorithm in order to divide the image into different layers. Afterwards, it uses the image gradient to estimate the blur kernel through an alternating iterative optimization strategy. The iteratively reweighted least squares algorithm (IRLS) is used to optimize the solution of the model. Finally, we use the unsharp masking algorithm to improve the high-frequency components of the image and enhance the edge and details of the image. Therefore, the algorithm can effectively remove the blur caused by the motion of the object, suppress the noise and ringing effect, and recover a higher quality clear image, which can be demonstrated on benchmark problems.