Sparse spectral signal reconstruction for one proposed nine-band multispectral imaging system | |
Sun, Bangyong1,2; Zhao, Zhe1; Xie, Dehong3; Yuan, Nianzeng1; Yu, Zhe1; Chen, Fuwei1; Cao, Congjun1; de Dravo, Vincent Whannou1 | |
刊名 | MECHANICAL SYSTEMS AND SIGNAL PROCESSING |
2020-07 | |
卷号 | 141 |
关键词 | Multispectral imaging system Image demosaicking Guide filter Filter array |
ISSN号 | 0888-3270 |
DOI | 10.1016/j.ymssp.2020.106627 |
产权排序 | 1 |
英文摘要 | Multispectral filter array (MSFA) imaging with one single sensor is a portable and inexpensive means of acquiring spectral image which is widely used for object detection, material analysis and mechanical system diagnosis. The most challenging task for MSFA imaging is the multispectral demosaicking with the aim of reconstructing the captured raw/mosaic image, especially for the systems with many bands which result in higher sparseness of the raw data. In this paper, we present a 9-band MSFA imaging system in a repetitive 4 x 4 filter array on a single sensor, and propose a demosaicking algorithm for reconstructing the raw spectral image. Within the 4 x 4 MSFA pattern, the fifth spectral band takes up half of the total spatial position while the remaining eight bands occupy 1/16 respectively. To reconstruct the sparse raw data, we first recover the fifth band by propagating the neighboring sampled pixels to the unsampled position using the image gradients, and then employ the reconstructed fifth band as a guided image to demosaick the other bands with the guided filter and residual interpolation. Finally, we estimate the spectral reflectance values from the multispectral image and the characterization matrix. In the experiment, we evaluate the performance of the 9-band imaging system with the binary tree-based edge-sensing (BTES) algorithm, compressed sensing (CS) algorithm, and our proposed demosaicking algorithm. The experiment results demonstrate that our demosaicking algorithm not only outperforms BTES and CS algorithms in terms of objective image quality, e.g., PSNR values and spectral errors, but also reduces the demosaicking artifacts in terms of subjective evaluations. (C) 2020 Elsevier Ltd. All rights reserved. |
语种 | 英语 |
出版者 | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD |
WOS记录号 | WOS:000529084500024 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/93419] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
作者单位 | 1.Xian Univ Technol, Sch Printing Packaging & Digital Media, Xian, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian, Peoples R China 3.Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing, Peoples R China |
推荐引用方式 GB/T 7714 | Sun, Bangyong,Zhao, Zhe,Xie, Dehong,et al. Sparse spectral signal reconstruction for one proposed nine-band multispectral imaging system[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING,2020,141. |
APA | Sun, Bangyong.,Zhao, Zhe.,Xie, Dehong.,Yuan, Nianzeng.,Yu, Zhe.,...&de Dravo, Vincent Whannou.(2020).Sparse spectral signal reconstruction for one proposed nine-band multispectral imaging system.MECHANICAL SYSTEMS AND SIGNAL PROCESSING,141. |
MLA | Sun, Bangyong,et al."Sparse spectral signal reconstruction for one proposed nine-band multispectral imaging system".MECHANICAL SYSTEMS AND SIGNAL PROCESSING 141(2020). |
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