论文标题
图像DeNoising用于强烈高斯噪声，具有专门的CNN，用于不同频率组件
Image Denoising for Strong Gaussian Noises With Specialized CNNs for Different Frequency Components
论文作者
论文摘要
在机器学习方法中，训练了网络的图像denoising网络，以从嘈杂的图像中恢复干净的图像。在本文中，基于培训多个专业网络而不是基于单个网络的现有结构，提出了一种新颖的结构。提出的模型是训练非常深网络的替代方法，以避免诸如消失或爆炸梯度之类的问题。通过将一个非常深的网络分为两个较小的网络，将提供相同数量的可学习参数，但是应该训练两个较小的网络，这些网络更容易训练。过度平滑和蜡质工件是现有方法的主要问题；因为该网络试图在一般结构和详细信息中保持卑鄙的正方形误差（MSE）低（MSE），这会导致细节的忽略。在存在强噪声的情况下，这个问题更为严重。为了减少此问题，在提出的结构中，图像被分解为其低频和高频组件，每个组件都用于训练单独的降级卷积神经网络。一个网络专门重建图像的一般结构，另一个网络专门重建细节。所提出的方法的结果表明，与存在强噪声相比，与流行的DeNoising方法相比，与流行的DeNoising方法相比，较高的峰信号与噪声比（PSNR）和结构相似性指数（SSIM）。
In machine learning approach to image denoising a network is trained to recover a clean image from a noisy one. In this paper a novel structure is proposed based on training multiple specialized networks as opposed to existing structures that are base on a single network. The proposed model is an alternative for training a very deep network to avoid issues like vanishing or exploding gradient. By dividing a very deep network into two smaller networks the same number of learnable parameters will be available, but two smaller networks should be trained which are easier to train. Over smoothing and waxy artifacts are major problems with existing methods; because the network tries to keep the Mean Square Error (MSE) low for general structures and details, which leads to overlooking of details. This problem is more severe in the presence of strong noise. To reduce this problem, in the proposed structure, the image is decomposed into its low and high frequency components and each component is used to train a separate denoising convolutional neural network. One network is specialized to reconstruct the general structure of the image and the other one is specialized to reconstruct the details. Results of the proposed method show higher peak signal to noise ratio (PSNR), and structural similarity index (SSIM) compared to a popular state of the art denoising method in the presence of strong noises.
