Luminance driven sparse representation based demosaicking
Luminance driven sparse representation based demosaicking
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DOI：
10.1109/icip.2014.7025358
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
Mattia Rossi;G. Calvagno
中科院分区：
文献类型：
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作者：
Mattia Rossi;G. Calvagno
典型的消费者相机在每个像素处仅感测彩色图像的表示所需的三个颜色分量中的一个。然后，通过称为去马赛克的过程来估计丢失的分量。最近传播的稀疏正则化方法的信号重建的目的已经扩展到去马赛克算法。本文从最近出现的一种基于稀疏表示的去马赛克算法出发，设计了一种新的去马赛克算法。该算法有效地估计原始亮度分量从采集的数据，然后用它来指导稀疏的全分辨率图像的重建。这种混合的方法去马赛克允许新算法优于过去的一个，并与领先的去马赛克算法竞争，无论是在PSNR测量和视觉质量。
Typical consumer cameras sense at each pixel only one out of the three color components the representation of a color image requires. Then the missing components are estimated via a procedure referred to as demosaicking. The recent spread of sparse regularization approaches for signal reconstruction purposes has extended to demosaicking algorithms too. In this paper, starting from a sparse representation based demosaicking algorithm recently appeared in the literature, a new one is devised. The proposed algorithm effectively estimates the original luminance component from the acquired data and then uses it to guide the sparsity based reconstruction of the full-resolution image. This hybrid approach to demosaicking allows the new algorithm to outperform the past one and to compete with leading demosaicking algorithms, both in terms of PSNR measure and visual quality.