Mihoubi Sofiane, Losson Olivier, Mathon Benjamin, Macaire Ludovic
J Opt Soc Am A Opt Image Sci Vis. 2018 Sep 1;35(9):1532-1542. doi: 10.1364/JOSAA.35.001532.
To discriminate gray-level texture images, spatial texture descriptors can be extracted using the local binary pattern (LBP) operator. This operator has been extended to color images at the expense of increased memory and computation requirements. Some authors propose to compute texture descriptors directly from raw images provided through a Bayer color filter array, which both avoids the demosaicking step and reduces the descriptor size. Recently, multispectral snapshot cameras have emerged to sample more than three wavelength bands using a multispectral filter array. Such cameras provide a raw image in which a single spectral channel value is available at each pixel. In this paper we design a local binary pattern operator that jointly extracts the spatial and spectral texture information directly from a raw image. Extensive experiments on a large dataset show that the proposed descriptor has both reduced computation cost and high discriminative power with regard to classical LBP descriptors applied to demosaicked images.
为了区分灰度纹理图像，可以使用局部二值模式（LBP）算子提取空间纹理描述符。该算子已扩展到彩色图像，但代价是增加了内存和计算需求。一些作者建议直接从通过拜耳彩色滤光片阵列提供的原始图像中计算纹理描述符，这既避免了去马赛克步骤，又减小了描述符的大小。最近，多光谱快照相机出现了，它使用多光谱滤光片阵列对三个以上的波长带进行采样。这种相机提供了一种原始图像，其中每个像素都有一个单一的光谱通道值。在本文中，我们设计了一种局部二值模式算子，它可以直接从原始图像中联合提取空间和光谱纹理信息。在一个大型数据集上进行的大量实验表明，与应用于去马赛克图像的经典LBP描述符相比，所提出的描述符既降低了计算成本，又具有较高的判别能力。
