7 March 2013 Auto-focus algorithm based on statistical blur estimation
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Conventional auto-focus techniques in movable-lens camera systems use a measure of image sharpness to determine the lens position that brings the scene into focus. This paper presents a novel wavelet-domain approach to determine the position of best focus. In contrast to current techniques, the proposed algorithm estimates the level of blur in the captured image at each lens position. Image blur is quantified by fitting a Generalized Gaussian Density (GGD) curve to a high-pass version of the image using second-order statistics. The system then moves the lens to the position that yields the least measure of image blur. The algorithm overcomes shortcomings of sharpness-based approaches, namely, the application of large band-pass filters, sensitivity to image noise and need for calibration under different imaging conditions. Since noise has no effect on the proposed blur metric, the algorithm works with a short filter and is devoid of parameter tuning. Furthermore, the algorithm could be simplified to use a single high-pass filter to reduce complexity. These advantages, along with the optimization presented in the paper, make the proposed algorithm very attractive for hardware implementation on cell phones. Experiments prove that the algorithm performs well in the presence of noise as well as resolution and data scaling.
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Prajit Kulkarni, Prajit Kulkarni, "Auto-focus algorithm based on statistical blur estimation ", Proc. SPIE 8667, Multimedia Content and Mobile Devices, 86671G (7 March 2013); doi: 10.1117/12.2008594; https://doi.org/10.1117/12.2008594


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