Adaptive variance based sharpness computation for low contrast images

Authors:
Xin Xu;Yinglin Wang;Jinshan Tang;Xiaolong Zhang;Xiaoming Liu
Affiliations:
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;Department of Computer Science and Engineering, School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Year:
2011

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Abstract

Low contrast images are easily suffering from noise effect. As a result, it can witness many local false peaks in the graph of sharpness function. However, the presence of many local false peaks hinders the camera's passive auto-focus system to perform its function in locating the focused peak. This paper presents an improved variance based sharpness computation which can adapt to various degrees of noise. The proposed sharpness computation can bring in the local false peaks generated by noise influence, and therefore produce a well defined focused peak standing for the best focused image. The experimental results from several image sequences validate the effectiveness of our proposed method.