Multi-exposure control method based on maximum local information entropy
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摘要: 基于融合多幅低动态图像来获取高动态图像的过程中，传统方法中低动态图像获取时对曝光时间选取的策略简单，使拍摄的多幅图像信息冗余较多，严重影响融合效率。提出了一种基于局部信息熵最大准则的多曝光控制方法。讨论了低动态场景图像信息熵与曝光时间的关系，得出了低动态范围场景的图像信息熵随曝光时间的增加呈现先增加后减小的规律，并在某个曝光时间处信息熵最大。对于高动态场景，首先，利用图像平均灰度响应与曝光时间的近似线性关系确定场景的曝光时间范围；然后，根据图像直方图将高动态场景分成若干个低动态范围场景区域；最后，以信息熵最大为优化目标，设计一维搜索算法，搜索各个低动态范围区域的最优曝光时间，直到所有区域都搜索到最优曝光时间。此方法将场景的局部信息熵与曝光时间联系起来，能针对不同的区域进行曝光时间优化，目的性强，有效地避免了传统曝光控制中的缺点，实验证明：用该方法获取的图像进行融合获得了良好的效果。Abstract: In the process of obtaining high dynamic range(HDR) image using the fusion of multiple shot images, the selection of exposure time in traditional method is blind, which makes the image information redundant and thus affects the fusion efficiency. In this paper, a method of multi-exposure control based on maximum local information entropy was proposed. The relationship between information entropy and exposure time of low dynamic scene was discussed. It was concluded that the image information entropy of a low dynamic range scene increased first and then decreased with the increase of exposure time. And information entropy achieved the maximum at a certain exposure time. For a high dynamic range scene, firstly, the range of exposure time was determined by using the approximate linear relationship between the gray level of the image and the exposure time. Secondly, the high dynamic range scene was divided into several low dynamic range(LDR) regions by using the histogram of the image. At last, the optimal exposure time of each region was searched. The method combined the local information entropy with the exposure time, which maked different exposure to different regions and avoided the shortcomings of the traditional exposure control effectively. Experimental results show that the image obtained with the proposed method has a good effect.
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Key words:
- auto-exposure control /
- HDR imaging /
- information entropy /
- one-dimensional search
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