采用光流估计的数字相机自动对焦算法  

Auto-focus algorithm of digital camera based on optical flow estimation

作　　者：郭惠楠[1] 曹剑中[1] 周祚峰[1] 董小坤[1] 刘庆[1] 马楠[2] 

机构地区：[1]中国科学院西安光学精密机械研究所,陕西西安710119 [2]空军工程大学理学院,陕西西安710051

出　　处：《红外与激光工程》2013年第12期3417-3422,共6页Infrared and Laser Engineering

基　　金：国家自然科学基金(61201376)

摘　　要：自动对焦技术对于数字相机至关重要,它是获取清晰图像的重要手段。针对复杂环境下多目标场景图像,提出了一种基于光流场估计的自动对焦算法。通过计算输入图像序列的光流场,对场景中的运动目标进行检测,根据目标运动属性准确判断出感兴趣目标。改进了Brenner清晰度评价方法,利用目标的二维边缘梯度信息建立评价函数,并且通过非线性增益提高评价函数的灵敏度,减小了噪声对评价值的影响。实验证明,该算法能够在主辅目标景深比达50倍的情况下分辨出感兴趣主目标,并在方差为0.02的随机噪声干扰下能有效地评价图像的清晰度;此算法将Brenner等评价函数的峰值稳定余量提高了1至4倍,对于不同图像具有良好的鲁棒性,易于硬件实现。Auto-Focus technique is a main approach to hunt clear images which plays an important role in digital camera application. According to several unknown target under complicated condition, a novel auto-focus algorithm was proposed based on optical flow estimation. By calculating the optical flow of each input frame, the moving targets in scene image were tested as well as according to the moving characteristic, the interested real target was judged. Brenner sharpness evaluation method was improved. Meanwhile the evaluation function was established using two dimensions edge-gradient information. The response sensitivity of evaluation function was also increased via nonlinear-gain coefficient the impact of noise on evaluation value was decreased. Experimental results show that the proposed method can distinguish the interested main target in 50 times depths of field of different targets and evaluate the definition of varied images with random noise in 0.02 variance value effectively. And it is of a good ability of robustness for different images, Brenner function improves the peak stability margin 1 to 4 times by the algorithm, and it can be easily achieved on hardware.

关 键 词：光学工程 自动对焦 光流 清晰度评价 数字相机 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]