IEEE Trans Image Process. 2013 Nov;22(11):4161-9. doi: 10.1109/TIP.2013.2271114. Epub 2013 Jun 26.
Advances in machine learning technology have made efficient image denoising possible. In this paper, we propose a new ridgelet support vector machine (RSVM) for image noise reduction. Multiscale ridgelet support vector filter (MRSVF) is first deduced from RSVM, to produce a multiscale, multidirection, undecimated, dyadic, aliasing, and shift-invariant geometric multiscale ridgelet support vector transform (GMRSVT). Then, multiscale dictionaries are learned from examples to reduce noises existed in GMRSVT coefficients. Compared with the available approaches, the proposed method has the following characteristics. The proposed MRSVF can extract the salient features associated with the linear singularities of images. Consequently, GMRSVT can well approximate edges, contours and textures in images, and avoid ringing effects suffered from sampling in the multiscale decomposition of images. Sparse coding is explored for noise reduction via the learned multiscale and overcomplete dictionaries. Some experiments are taken on natural images, and the results show the efficiency of the proposed method.
机器学习技术的进步使得高效的图像去噪成为可能。在本文中，我们提出了一种新的脊波支持向量机（RSVM）用于图像降噪。首先从 RSVM 推导出多尺度脊波支持向量滤波器（MRSVF），以产生多尺度、多方向、非下采样、二进、混叠和平移不变的几何多尺度脊波支持向量变换（GMRSVT）。然后，从示例中学习多尺度字典以减少 GMRSVT 系数中的噪声。与现有方法相比，所提出的方法具有以下特点。所提出的 MRSVF 可以提取与图像线性奇异性相关的显著特征。因此，GMRSVT 可以很好地逼近图像中的边缘、轮廓和纹理，并避免在图像的多尺度分解中采样引起的振铃效应。通过学习的多尺度和过完备字典探索稀疏编码以进行降噪。对自然图像进行了一些实验，结果表明了所提出方法的有效性。
