Rapid and Accurate Local Gaussian Noise Removal
Rapid and Accurate Local Gaussian Noise Removal
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发表时间：
2020-12
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通讯作者：
Shogo Seta;Yusuke Nakahara;Takuro Yamaguchi;M. Ikehara
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作者：
Shogo Seta;Yusuke Nakahara;Takuro Yamaguchi;M. Ikehara
本文提出了一种快速、高精度的高斯噪声去除方法，该方法采用了RAISR中用于超分辨率的学习线性滤波器。我们的算法是一个快速的本地方法，但产生可比的结果，以其高精度而闻名的非本地方法的准确性。本文的新奇在于，与超分辨率相同的处理被纳入去噪。传统的局部处理包括平滑处理，并且具有在降低噪声的同时丢失原始信号的高频分量的问题。为了解决这个问题，该方法结合了一种超分辨率方法，该方法补偿高频分量作为后处理。超分辨率方法利用了一个过程，根据RAISR中的补丁的特点，应用学习线性滤波器。由于该方法是局部处理，因此与BM 3D等非局部处理方法相比，运算速度快。
In this paper, we propose a rapid and high-accuracy Gaussian noise removal method by applying the learning linear filter used in RAISR for super-resolution. Our algorithm is a rapid local method, yet produces comparable results to the accuracy of the non-local method known for its high accuracy. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The conventional local processing includes smoothing processing, and has a problem that high-frequency components of an original signal are lost while reducing the noise. In order to solve the problem, this method incorporates a super-resolution method that compensates for high-frequency components as post-processing. The super-resolution method utilizes a process that applies a learning linear filter according to the feature of patches in RAISR. Because the proposed method consists of local precessing, its operation is rapid compared to non local processing like BM3D.