论文标题
设计和培训双CNN用于图像denoising
Designing and Training of A Dual CNN for Image Denoising
论文作者
论文摘要
用于图像DeNoising的深度卷积神经网络（CNN）最近引起了研究的兴趣。但是，普通网络无法恢复复杂任务的细节，例如真实的嘈杂图像。在本文中，我们提出了一个双重剥离网络（Dudenet）来恢复干净的图像。具体而言，Dudenet由四个模块组成：一个特征提取块，增强块，压缩块和重建块。具有稀疏马奇主义的特征提取块通过两个子网络提取全球和本地特征。增强块聚集并融合全球和本地功能，为后一种网络提供互补信息。压缩块完善了提取的信息并压缩网络。最后，重建块被用来重建一个变性图像。 Dudenet具有以下优点：（1）具有解析机制的双网络可以提取互补特征，以增强DeOiser的广义能力。 （2）融合全球和本地功能可以提取出色的功能，以恢复复杂的嘈杂图像的细节。 （3）使用小型过滤器来降低DeOiser的复杂性。广泛的实验表明，达德内特的优越性比现有的当前最新降解方法的优越性。
Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In this paper, we propsoed a Dual denoising Network (DudeNet) to recover a clean image. Specifically, DudeNet consists of four modules: a feature extraction block, an enhancement block, a compression block, and a reconstruction block. The feature extraction block with a sparse machanism extracts global and local features via two sub-networks. The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network. The compression block refines the extracted information and compresses the network. Finally, the reconstruction block is utilized to reconstruct a denoised image. The DudeNet has the following advantages: (1) The dual networks with a parse mechanism can extract complementary features to enhance the generalized ability of denoiser. (2) Fusing global and local features can extract salient features to recover fine details for complex noisy images. (3) A Small-size filter is used to reduce the complexity of denoiser. Extensive experiments demonstrate the superiority of DudeNet over existing current state-of-the-art denoising methods.
