Abstract
In view of the problems of uneven exposure in the image acquisition and the serious loss of details in the traditional multi-exposure image fusion algorithm, a method of image fusion with details preservation is proposed. A weighted approach to multi-exposure image fusion is used, taking into account the features such as local contrast, exposure brightness, and color information to better preserve detail. For the purpose of eliminating the noise and interference, using the recursive filter to filter. Compared with other algorithms, the proposed algorithm can retain the rich detail information to meet the quality requirements of spot welding image fusion and has certain application value.
Highlights
In recent years, image fusion has become a key research area in the field of information fusion
The multi-exposure image fusion algorithm based on the S-curve is proposed by Fuzheng Fang et al, But it has a great limitation and can only improve the fusion effect to some extent [1], Mertens et al proposed Laplacian-based exposure fusion, but this method could not take into account the global brightness and local details [2]
For the purpose of preserving the details of the images better to show the richer image information, this paper proposes a multi-exposure image fusion method with detail preservation
Summary
Image fusion has become a key research area in the field of information fusion. Pixel-based multi-exposure image fusion algorithm can be used to fuse the most basic pixel units and extract the accurate details of the image. Shen et al Proposed a multi-exposure image fusion algorithm based on the improved pyramid, which can remain more details , But the algorithm is more difficult to calculate[3]. Sujoy et al Proposed multi-exposure and multi-focus image fusion algorithms [4]. For the purpose of preserving the details of the images better to show the richer image information, this paper proposes a multi-exposure image fusion method with detail preservation. Three characteristic indexes of the image are calculated: local contrast, exposure brightness and color information, the results of
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