Optimized Color Filter Arrays for Sparse Representation-Based Demosaicking
Optimized Color Filter Arrays for Sparse Representation-Based Demosaicking
复制标题
用于基于稀疏表示的去马赛克的优化滤色镜阵列
DOI：
10.1109/tip.2017.2679440
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发表时间：
2017
影响因子：
10.6
通讯作者：
Yu Jian
中科院分区：
文献类型：
--
作者：
Li Jia;Bai Chenyan;Lin Zhouchen;Yu Jian
去马赛克是从由数字彩色相机捕获的原始图像重建彩色图像的问题，该数字彩色相机用彩色滤光片阵列（CFA）覆盖其唯一的成像传感器。基于稀疏表示的去马赛克已被证明产生上级重建质量。然而，几乎所有现有的算法在这一类别中使用的CFA，这不是专门优化的算法。在本文中，我们考虑优化设计CFAs稀疏表示为基础的去马赛克，字典是精心挑选的。CFAs对应于压缩感知中使用的投影矩阵的事实启发我们通过最小化互相干性来优化CFAs。这比传统的投影矩阵更具挑战性，因为CFA具有物理可实现性约束。然而，大多数现有的方法，以最小化的互相关要求的投影矩阵应该是无约束的，使它们不适用于设计CFA。我们认为直接最小化的相互相干与CFA的物理可实现性约束作为一个广义分式规划问题，它需要找到足够精确的解决方案，一系列的非凸非光滑最小化问题。我们采用重新分布的近端束方法来解决这个问题。在基准图像上的实验证明了该方法的优越性。特别是，我们证明了一个简单的基于稀疏表示的去马赛克算法与我们专门优化的CFA可以优于LSSC [1]。据我们所知，它是第一个基于稀疏表示的去马赛克算法，在CPSNR方面击败LSSC。
Demosaicking is the problem of reconstructing a color image from the raw image captured by a digital color camera that covers its only imaging sensor with a color filter array (CFA). Sparse representation-based demosaicking has been shown to produce superior reconstruction quality. However, almost all existing algorithms in this category use the CFAs, which are not specifically optimized for the algorithms. In this paper, we consider optimally designing CFAs for sparse representation-based demosaicking, where the dictionary is well-chosen. The fact that CFAs correspond to the projection matrices used in compressed sensing inspires us to optimize CFAs via minimizing the mutual coherence. This is more challenging than that for traditional projection matrices because CFAs have physical realizability constraints. However, most of the existing methods for minimizing the mutual coherence require that the projection matrices should be unconstrained, making them inapplicable for designing CFAs. We consider directly minimizing the mutual coherence with the CFA’s physical realizability constraints as a generalized fractional programming problem, which needs to find sufficiently accurate solutions to a sequence of nonconvex nonsmooth minimization problems. We adapt the redistributed proximal bundle method to address this issue. Experiments on benchmark images testify to the superiority of the proposed method. In particular, we show that a simple sparse representation-based demosaicking algorithm with our specifically optimized CFA can outperform LSSC [1]. To the best of our knowledge, it is the first sparse representation-based demosaicking algorithm that beats LSSC in terms of CPSNR.