Selectively detail-enhanced exposure fusion via a gradient domain content adaptive bilateral filter

Selectively detail-enhanced exposure fusion via a gradient domain content adaptive bilateral filter
Title:
Selectively detail-enhanced exposure fusion via a gradient domain content adaptive bilateral filter
Other Titles:
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI:
10.1109/ICASSP.2014.6854041
Publication Date:
04 May 2014
Citation:
Z. G. Li and J. H. Zheng, "Selectively detail-enhanced exposure fusion via a gradient domain content adaptive bilateral filter," 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, 2014, pp. 2455-2459. doi: 10.1109/ICASSP.2014.6854041
Abstract:
Bilateral filters suffer from halo artifacts when they are applied for image enhancement. In this paper, a new bilateral filter is proposed in gradient domain to address this problem. Both spatial similarity parameter and intensity similarity parameter of the proposed filter are spatially varying instead of being fixed as in the existing bilateral filters. As a result, it can preserve edges and smooth flat areas better than the existing bilateral filters. The proposed filter is then adopted to design a selectively detail-enhanced exposure fusion algorithm. Fine details of multiple differently exposed images are extracted simultaneously using the proposed filter. Instead of amplifying and adding all extracted fine details to an intermediate image which is fused by an existing exposure fusion algorithm, the fine details in all areas except flat ones are amplified and added to the intermediate image. The resultant algorithm can reduce halo artifacts and prevent noise in flat areas from being amplified in the final image. Therefore, the proposed algorithm fuses images with much better visual quality.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1520-6149
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