Multi-frame motion deblurring using coded exposure imaging with complementary fluttering sequences. (June 2020)

• Record Type: Journal Article
• Authors: Cui, Guangmang; Ye, Xiaojie; Zhao, Jufeng; Zhu, Liyao; Chen, Ying
• Is Part Of: Optics & laser technology. Volume 126(2020)
• Publication Date: 2020-06
• Subjects: Coded exposure -- Motion deblurring -- Complementary fluttering sequences -- Multi-frame image -- Computational imaging -- Image quality; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2020.106119
• Languages: English
• ISSNs: 0030-3992

Abstract

Highlights: A unified process framework is proposed to carry out multi-frame motion deblurring. A well-designed complementary fluttering sequence generated method is set up. An effective deblurring framework is performed to calculate the deblurred results. A real hardware imaging system is built to realize the acquisition of multi frames. Abstract: Motion blurring is one of the most common but unpleasant phenomenons along with the camera imaging process, resulting in image resolution degradation. Coded exposure technique is an effective computational imaging approach to handle with this problem by introducing extra shutter control within the exposure time and help the deblurring problem become well-posed. In this paper, a multi-frame motion deblurring framework is presented using coded exposure imaging with complementary fluttering sequences. A set of complementary sequences are well designed to compensate for the spatial losses due to motion blurring in each frame based on the complementary theory of Golay matrix pair. Hadamard matrix is served as the Golay sequence pair to generate complementary fluttering sequences to capture multi-frame images for the following deblurring processing. The joint modulation transfer function (MTF) of the complementary code sequences has a more flat spectrum curve with smaller variance value and higher MTF value. For multi-frame deblurring implementation, a unified multi-image deblurring framework is performed by introducing Total Variation (TV) regularization prior with each selected code sequence in the complementary set integrated in the energy function. A real imaging system is built and various groups of synthetic and real-world imaging experiments are carried out to verify the effectiveness of the method. Experimental results demonstrate that the deblurrring images obtained by the proposed approach are of high quality with improved objective image evaluation values and better negative artifacts suppression.