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Title: A new polarization image demosaicking algorithm by exploiting inter-channel correlations with guided filtering
Authors: Liu, Shumin
Chen, Jiajia
Xun, Yuan
Zhao, Xiaojin
Chang, Chip-Hong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Liu, S., Chen, J., Xun, Y., Zhao, X., & Chang, C.-H. (2020). A new polarization image demosaicking algorithm by exploiting inter-channel correlations with guided filtering. IEEE Transactions on Image Processing, 29, 7076-7089. doi:10.1109/TIP.2020.2998281
Project: MOE2018-T1- 001-131, RG87/18-(S)
Journal: IEEE Transactions on Image Processing
Abstract: This paper presents a fast and effective polarization image demosaicking algorithm, which explores inter-channel dependency of Stokes parameters for the minimization of residual aliasing artifacts after cubic spline interpolation. A guided filtering approach is used for denoising. An optimization based on the confidence level of the aforementioned guided filtering, the correlations between the demosaicked image and input, as well as the total intensity, angle and degree of linear polarization, is constructed and solved with Newton’s method. Experimental results demonstrate that the proposed algorithm can surpass the existing methods in terms of both objective root mean squared error and structural similarity index by at least 36.0% and 3.4%, respectively, and by close visual inspection of the clarity of objects in the angle and degree of linear polarization images. The proposed algorithm consists of only convolutions and elementwise operations, making it fast and parallelizable for efficient GPU acceleration. An image of size 512×612×4 can be processed within 10 s on i7-6700k CPU, and gains further 5 times speedup with M4000M GPU.
ISSN: 1941-0042
DOI: 10.1109/TIP.2020.2998281
Schools: School of Electrical and Electronic Engineering 
Research Centres: Centre for Integrated Circuits and Systems 
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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