Adaptive variance based sharpness computation for low contrast images

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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. © 2011 Springer-Verlag.

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APA

Xu, X., Wang, Y., Tang, J., Zhang, X., & Liu, X. (2011). Adaptive variance based sharpness computation for low contrast images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6838 LNCS, pp. 335–341). https://doi.org/10.1007/978-3-642-24728-6_45

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