A total variation and group sparsity-based algorithm for nuclear radiation-contaminated video restoration

ABSTRACT

Nuclear radiation-contaminated video deblurring is an important issue of the robot vision system and has been widely studied. In this paper, a hybrid radiation-contaminated video frame enhancement algorithm is proposed that utilizes both intra-frame and inter-frame correlation by a two-stage strategy. In the first stage, total variation (TV) transformation are used to locate the spot areas, and then local TV is employed to restore spot areas. The preliminary deblurring result not only enhances the video frame and similar patch matching accuracy but also provides reliable estimates of filtering parameters. In the second stage, visual group technology and improved k-nearest neighbours (k-NN) method is used to select similar frames and reference patches respectively. The final enhanced video frame is obtained by a novel patch-based group sparse method. Experimental results clearly show that the proposed method outperforms other state-of-the-art methods in both quantitative evaluation indices and visual quality measurements.

KEYWORDS:

• Image enhancement
• patch correlations
• total variation
• split Bregman
• frames retrieval
• Bayesian mean squared error
• radiation-contaminated image
• group sparsity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the Project of Sichuan Provincial Department of Science and Technology (2020JDJQ0075), the Project of Wuliangye Group (CXY2020ZR006) and the Project of Artificial Intelligence Key Laboratory of Sichuan Province (2020RZY02).

Notes on contributors

Mingju Chen

Mingju Chen (1982-) received the Ph.D. degree in Southwest University of Science and Technology. He is currently an associate professor in Sichuan University of Science and Engineer. His research interests include machine vision inspection systems and image processing.

Hua Zhang

Hua Zhang (1969-) received his PhD degree in Chongqing University in 2006. He is currently a professor in Southwest University of Science and Technology. His research interests include Nuclear detection technology, robot technology, and machine vision inspection systems.

Liuman Lu

Liuman Lu (1982-) is currently a lecturer in Southwest University of Science and Technology and pursuing the Ph.D. degree in University of Science and Technology of China. His research interests include Nuclear detection technology and robot technology.

Hao Wu

Hao Wu (1980-) received his PhD degree in College of electrical engineering from Southwest Jiaotong University in 2016. He is currently a professor in Sichuan University of Science & Engineering. His research interests include Power system automation, fault diagnosis technology, and artificial intelligence.