A Precise Multi-Exposure Image Fusion Method Based on Low-level Features

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-03, Vol.20 (6), p.1597
Main Authors: Qi, Guanqiu, Chang, Liang, Luo, Yaqin, Chen, Yinong, Zhu, Zhiqin, Wang, Shujuan
Format: Article
Language:English
Subjects:
a priori exposure quality
Algorithms
Computer vision
Consistency tests
Decomposition
Exposure
ghost removal
high-dynamic-range imaging
image fusion
Image processing
Methods
multi-exposure image fusion
Probability distribution
Quality
Registration
Saturation (color)
Sharpness
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20061597