A structure-adaptive vector filter for removal of impulse noise from color images is presented. The proposed method is based on local orientation estimation. A color image is represented in quaternion form, and then, quaternion Fourier transform is used to compute the orientation of the pattern in a local neighborhood. Since the computation in quaternion frequency domain is extremely time-consuming, we prove a theorem that the integral of the product of frequency variables and the magnitude of quaternion frequency signals can be computed directly in spatial domain, which results that the color orientation detection problem can be solved in spatial domain. Based on the local orientation and orientation strength, the size, shape, and orientation of the support window of vector median filter (VMF) are adaptively determined, leading to an effective structure-adaptive VMF. Unlike the classical VMF restricting the output to the existing color samples, this paper computes the output of VMF over the entire 3D data space, which boosts the filtering performance effectively. To further improve denoising effect, a deep convolutional neural network is employed to detect impulse noise in color images and integrated into the proposed denoising framework. The experimental results exhibit the effectiveness of the proposed denoiser by showing significant performance improvements both in noise suppression and in detail preservation, compared to other color image denoising methods.

中文翻译：

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基于局部方向估计和CNN分类器的彩色图像去噪

提出了一种结构自适应的矢量滤波器，用于从彩色图像中去除脉冲噪声。所提出的方法基于局部取向估计。彩色图像以四元数形式表示，然后，使用四元数傅里叶变换来计算图案在局部邻域中的方向。由于四元数频域的计算非常耗时，我们证明了一个定理，可以直接在空间域中计算频率变量与四元数频率信号幅度的乘积的积分，从而导致颜色方向检测问题可以在空间领域解决。根据局部方向和方向强度，自适应确定矢量中值滤波器（VMF）的支持窗口的大小，形状和方向，导致有效的结构自适应VMF。与传统的VMF将输出限制为现有的颜色样本不同，本文在整个3D数据空间上计算VMF的输出，从而有效地提高了过滤性能。为了进一步提高去噪效果，采用了深度卷积神经网络来检测彩色图像中的脉冲噪声并将其集成到所提出的去噪框架中。与其他彩色图像降噪方法相比，实验结果通过在噪声抑制和细节保留方面均显示出显着的性能改进，从而展示了所提出的降噪器的有效性。有效地提高了过滤性能。为了进一步提高去噪效果，采用了深度卷积神经网络来检测彩色图像中的脉冲噪声并将其集成到所提出的去噪框架中。与其他彩色图像降噪方法相比，实验结果通过在噪声抑制和细节保留方面均显示出显着的性能改进，从而展示了所提出的降噪器的有效性。有效地提高了过滤性能。为了进一步提高去噪效果，采用了深度卷积神经网络来检测彩色图像中的脉冲噪声并将其集成到所提出的去噪框架中。与其他彩色图像降噪方法相比，实验结果通过在噪声抑制和细节保留方面均显示出显着的性能改进，从而展示了所提出的降噪器的有效性。