Due to the superior performance and fast running speed, deep learning methods have been widely employed in image processing fields. However, most deep learning-based denoising methods require a specific noise level to train their models, and denoising models are built to remove specific levels of noise, which lacks the flexibility to deal with spatially variant noise. In this paper, we present a discrete shearlet transform (DST)-based denoising convolutional network (DSTnet). The proposed method first decomposes an image by DST and gets several subband images, then these subband images are used as input samples into the convolutional neural networks (CNNs) blocks. The proposed method has a good compromise between denoising performance and computation time. The DSTnet not only has a good efficiency in noise removing and detail preservation but also has the ability to handle a wide range of noise levels, which are suitable for real image denoising.

由于卓越的性能和快速的运行速度，深度学习方法已广泛应用于图像处理领域。但是，大多数基于深度学习的降噪方法都需要特定的噪声水平来训练其模型，而降噪模型的构建是为了去除特定的噪声水平，而该模型缺乏处理空间变异噪声的灵活性。在本文中，我们提出了一种基于离散剪切波变换（DST）的去噪卷积网络（DSTnet）。所提出的方法首先通过DST分解图像并获得几个子带图像，然后将这些子带图像用作卷积神经网络（CNN）块的输入样本。所提出的方法在降噪性能和计算时间之间有很好的折衷。