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使用新对比度度量的用于低对比度图像的鲁棒自动对焦算法。

Robust automatic focus algorithm for low contrast images using a new contrast measure.

机构信息

Department of Computer Science and Engineering, Shanghai Jiao Tong University, NO. 800 Dongchuan Road, Shanghai 200240, China.

出版信息

Sensors (Basel). 2011;11(9):8281-94. doi: 10.3390/s110908281. Epub 2011 Aug 25.

Abstract

Low contrast images, suffering from a lack of sharpness, are easily influenced by noise. As a result, many local false peaks may be generated in contrast measurements, making it difficult for the camera's passive auto-focus system to perform its function of locating the focused peak. In this paper, a new passive auto-focus algorithm is proposed to address this problem. First, a noise reduction preprocessing is introduced to make our algorithm robust to both additive noise and multiplicative noise. Then, a new contrast measure is presented to bring in local false peaks, ensuring the presence of a well defined focused peak. In order to gauge the performance of our algorithm, a modified peak search algorithm is used in the experiments. The experimental results from an actual digital camera validate the effectiveness of our proposed algorithm.

摘要

低对比度图像,由于缺乏清晰度,容易受到噪声的影响。因此,在对比度测量中可能会产生许多局部虚假峰值,使得相机的被动自动对焦系统难以执行其定位焦点峰值的功能。在本文中,提出了一种新的被动自动对焦算法来解决这个问题。首先,引入降噪预处理,使我们的算法对加性噪声和乘性噪声都具有鲁棒性。然后,提出了一种新的对比度度量方法,引入局部虚假峰值,确保存在定义明确的焦点峰值。为了评估我们算法的性能,在实验中使用了改进的峰值搜索算法。实际数码相机的实验结果验证了我们提出的算法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/380f/3231510/0dbe52e3d1d7/sensors-11-08281f1.jpg

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