An effective motion-blurred image restoration approach for automated optical inspection

Abstract

This paper presents a real-time restoration method for linear local motion-blurred images in automated optical inspection (AOI). The proposed approach is to firstly divide such an image into many sub-images and then detect the blurred sub-image by the gradient distribution and the cepstrum maximum. For a blurred sub-image, the blur direction and blur length are estimated in order to calculate the parameters of the point spread function (PSF). The Richardson–Lucy deconvolution algorithm and Wiener filtering are employed to restore this blurred sub-image. Through experimentation, the proposed algorithm produces good results on blurred images caused by the linear motion AOI equipment. To test its performance, the proposed algorithm is compared with other approaches by using a real captured printed circuit board (PCB) image, and it is proven to be superior to the others in terms of accuracy.

Keywords:

• motion blur
• cepstrum
• point spread function
• image restoration
• automated optical inspection

Acknowledgements

The authors thank the editor and reviewers for their valuable comments and suggestions that have improved the paper's quality.

ORCID

C H Wu http://orcid.org/0000-0003-1259-4048

K K Tseng http://orcid.org/0000-0002-6258-7009

C K Ng http://orcid.org/0000-0001-9777-5096

W H Ip http://orcid.org/0000-0001-6609-0713

Additional information

Funding

This work was supported in part by the Innovation and Technology Commission (ITC) of the HKSAR Government of the People's Republic of China for this project [grant number: ITS/161/13FX]; and the Department of Computer Science, Harbin Institute of Technology (Shenzhen Graduate School). The authors would also like to thank the Research Committee and the Department of Industrial and Systems Engineering, the Hong Kong Polytechnic University for the financial assistance to the involved research student [grant number: RT3C].

Notes on contributors

C H Wu

Notes on contributors

Dr C H Wu received his BEng and Ph.D. degrees in Industrial and Systems Engineering from the Hong Kong Polytechnic University in 2006 and 2011, respectively. He is a senior member of the Hong Kong Society for Quality (HKSQ) and a member of the Institute of Electrical and Electronics Engineers (IEEE). His current research areas encompass evolutionary optimisation methods, image-processing techniques, Internet of Things (IoT) applications and quality management systems.

Kuo-kun Tseng

Dr K K Tseng received his M.E.Sc. degree in Computer Science and Information Engineering and D.E. degree in Computer Science and Information Engineering from the National Chiao Tung University, Taiwan in 2002 and 2006, respectively. He is an Associate Professor in the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen Graduate School). He has registered six patents and published more than 40 conference and journal papers. His research interests include biological IoT and mobile cloud computing systems, electrocardiogram (ECG), image and packet identification and classification algorithms.

C K Ng

Mr C K Ng received his BEng degree in 2008 from the Hong Kong Polytechnic University. He is currently pursuing a Ph.D. degree in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His current research areas include radio-frequency identification (RFID), wireless sensor networks (WSN) and IoT.

W H Ip

Dr W H Ip received his M.Sc. degree from Cranfield University, the UK in 1983, M.B.A. degree from Brunel University, the UK in 1989 and Ph.D. degree in Manufacturing Engineering from Loughborough University, the UK in 1993. He is currently an Associate Professor in the Industrial and Systems Engineering Department and Head of the RFID Solutions Laboratory at the Hong Kong Polytechnic University. Over the span of 20 years of work in industry, education and consulting, he has published more than 200 papers, with over 100 papers in SCI-indexed journals and over 80 papers in conference proceedings. His research interests include evolutionary computing, WSN, RFID systems and resilience engineering. Dr Ip is a member of the Institution of Engineering and Technology (IET), the Institution of Mechanical Engineers (IMechE) and the Hong Kong Institution of Engineers (HKIE), a senior member of IEEE, and a fellow of the Hong Kong Quality Management Asociation Limited (HKQMA).