论文标题
突出显示基于张量低级别和稀疏分解的镜面反射分离
Highlight Specular Reflection Separation based on Tensor Low-rank and Sparse Decomposition Using Polarimetric Cues
论文作者
论文摘要
本文涉及基于张量低秩分解框架的镜面反射去除，借助极化信息。我们的方法是通过观察到的观察，即图像的镜头亮点稀疏分布，而剩余的弥漫性反射可以通过使用低级和稀疏分解框架的几种不同颜色的线性组合很好地近似。与当前的溶液不同，我们的张量低级别分解可以保持镜面和弥漫性信息的空间结构，从而使我们能够在较强的镜面反射或饱和区域中恢复弥漫性图像。我们进一步定义并施加了新的极化正规化项，作为对颜色通道的约束。这种正则化可以通过处理颜色失真来提高该方法的性能，以恢复准确的弥散图像，这是一个基于色度的方法的常见问题，尤其是在强烈的镜面反射的情况下。通过对合成图像和实际极化图像的全面实验，我们证明了我们的方法能够显着提高突出显示镜面去除的准确性，并优于恢复弥漫性图像的竞争方法，尤其是在强烈的镜面反射或饱和区域的区域。
This paper is concerned with specular reflection removal based on tensor low-rank decomposition framework with the help of polarization information. Our method is motivated by the observation that the specular highlight of an image is sparsely distributed while the remaining diffuse reflection can be well approximated by a linear combination of several distinct colors using a low-rank and sparse decomposition framework. Unlike current solutions, our tensor low-rank decomposition keeps the spatial structure of specular and diffuse information which enables us to recover the diffuse image under strong specular reflection or in saturated regions. We further define and impose a new polarization regularization term as constraint on color channels. This regularization boosts the performance of the method to recover an accurate diffuse image by handling the color distortion, a common problem of chromaticity-based methods, especially in case of strong specular reflection. Through comprehensive experiments on both synthetic and real polarization images, we demonstrate that our method is able to significantly improve the accuracy of highlight specular removal, and outperform the competitive methods to recover the diffuse image, especially in regions of strong specular reflection or in saturated areas.
