Demosaicking for multispectral images based on vectorial total variation
Demosaicking for multispectral images based on vectorial total variation
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DOI：
10.1007/s10043-016-0221-y
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发表时间：
2016-05
期刊：
影响因子：
1.2
通讯作者：
Kazuma Shinoda;Taisuke Hamasaki;Maru Kawase;Madoka Hasegawa;Shigeo Kato
中科院分区：
文献类型：
--
作者：
Kazuma Shinoda;Taisuke Hamasaki;Maru Kawase;Madoka Hasegawa;Shigeo Kato
多光谱图像具有比RGB图像更多的色彩成分，可用于植被分析和医学成像领域。为了缩短捕获时间，降低成本，研究了一种多光谱滤波阵列（MSFA）捕获系统。在该系统中，对MSFA捕获的拼接图像进行解拼接以重建MSI。我们提出了一种基于矢量总变分（VTV）正则化的MSI去马赛克方法。这一过程被看作是图像观测模型的逆问题。在约束条件下，通过最小化VTV作为正则化项来估计重构图像。实验结果表明，无论在峰值信噪比还是结构相似度方面，采用该方法获得的重构图像质量都优于传统方法。
Multispectral images (MSIs), which consist of more color components than RGB images, can be used in the field of vegetation analysis and medical imaging. A capturing system with multispectral filter array (MSFA) technology has been researched to shorten the capturing time and reduce the cost. In this system, the mosaicked image captured by the MSFA is demosaicked to reconstruct the MSI. We propose a demosaicking method using vectorial total variation (VTV) regularization for an MSI. This process is regarded as inverse problem of the image observation model. The reconstructed image is estimated by minimizing the VTV as a regularization term under the constraint condition. In the experimental results, the reconstructed image quality obtained using the proposed method is better than that of the conventional approaches in terms of both peak signal-to-noise ratio and structural similarity.