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Joint Demosaicing and Denoising Based on Interchannel Nonlocal Mean Weighted Moving Least Squares Method

机译：基于Interchonnel非局部平均加权移动最小二乘法的联合去脱模和去噪

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摘要

Nowadays, the sizes of pixel sensors in digital cameras are decreasing as the resolution of the image sensor increases. Due to the decreased size, the pixel sensors receive less light energy, which makes it more sensitive to thermal noise. Even a small amount of noise in the color filter array (CFA) can have a significant effect on the reconstruction of the color image, as two-thirds of the missing data would have to be reconstructed from noisy data; because of this, direct denoising would need to be performed on the raw CFA to obtain a high-resolution color image. In this paper, we propose an interchannel nonlocal weighted moving least square method for the noise removal of the raw CFA. The proposed method is our first attempt of applying a two dimensional (2-D) polynomial approximation to denoising the CFA. Previous works make use of 2-D linear or directional 1-D polynomial approximations. The reason that 2-D polynomial approximation methods have not been applied to this problem is the difficulty of the weight control in the 2-D polynomial approximation method, as a small amount of noise can have a large effect on the approximated 2-D shape. This makes CFA denoising more important, as the approximated 2-D shape has to be reconstructed from only one-third of the original data. To address this problem, we propose a method that reconstructs the approximated 2-D shapes corresponding to the RGB color channels based on the measure of the similarities of the patches directly on the CFA. By doing so, the interchannel information is incorporated into the denoising scheme, which results in a well-controlled and higher order of polynomial approximation of the color channels. Compared to other nonlocal-mean-based denoising methods, the proposed method uses an extra reproducing constraint, which guarantees a certain degree of the approximation order; therefore, the proposed method can reduce the number of false reconstruction artifacts that often occur in nonlocal-mean-based denoising methods. Experimental results demonstrate the performance of the proposed algorithm.
机译：如今，随着图像传感器的分辨率增加，数码相机中的像素传感器的大小是减小的。由于尺寸减小，像素传感器接收较少的光能，这使得对热噪声更敏感。彩色滤光器阵列（CFA）中的少量噪声甚至可能对彩色图像的重建具有显着影响，因为必须从嘈杂数据重建三分之二的缺失数据;因此，需要在原始CFA上进行直接去噪，以获得高分辨率彩色图像。在本文中，我们提出了一种用于噪声去除原始CFA的噪声的非局部加权移动最小二乘法。所提出的方法是我们第一次尝试将二维（2-D）多项式近似施加到去噪CFA。以前的作品利用2-D线性或定向1-D多项式近似。 2-D多项式近似方法尚未应用于该问题的原因是2-D多项式近似方法中重量控制的难度，因为少量噪声可以对近似的2-D形效果很大。这使得CFA去噪更重要，因为必须仅从原始数据的三分之一重建近似的2-D形。为了解决这个问题，我们提出了一种方法，该方法基于直接在CFA上的斑块的相似性的测量来重建与RGB颜色信道对应的近似的2-D形状。通过这样做，交互信息被结合到去噪方案中，这导致良好控制的和更高阶的颜色通道的多项式近似。与其他非本种平均均值的去噪方法相比，所提出的方法使用额外的再现约束，这保证了一定程度的近似顺序;因此，所提出的方法可以减少经常发生在基于非本体均值的去噪方法的错误重建伪像的数量。实验结果证明了所提出的算法的性能。

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