Qi Guanqiu, Chang Liang, Luo Yaqin, Chen Yinong, Zhu Zhiqin, Wang Shujuan
Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA.
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel). 2020 Mar 13;20(6):1597. doi: 10.3390/s20061597.
Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing.
多曝光图像融合（MEF）提供了一种生成高动态范围（HDR）图像的简洁方法。尽管现有MEF方法在不同静态场景中能够实现精确融合，但在不同动态场景下，其相应的去重影性能有所不同。本文提出一种基于特征块的精确MEF方法（FPM），以提高动态场景下去重影的鲁棒性。首先通过先验曝光质量选择一幅参考图像，然后将其用于结构一致性测试，以解决动态场景MEF中存在的图像重影问题。利用引导滤波器将源图像分解为空间域结构。对分解后图像的基础层和细节层进行融合，以实现MEF。图像块的结构分解和适当的曝光评估被集成到所提出的解决方案中。对全局和局部曝光进行优化，以提高融合性能。与六种现有MEF方法相比，所提出的FPM不仅提高了动态场景下去重影的鲁棒性，而且在色彩饱和度、图像清晰度和局部细节处理方面也表现出色。
