Recently, a new convolutional neural network (CNN) architecture, dubbed as densely connected convolutional network (DenseNet), has shown excellent results on image classification tasks. The idea of DenseNet is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion, then the network will be more accurate and easier to train. In this study, we extend DenseNet to deal with the problem of impulse noise reduction. It aims to explore the densely connected network for impulse noise removal (DNINR), which utilizes CNN to learn pixel-distribution features from noisy images. Compared with the traditional median filter-based and variational regularization methods that utilize the spatial neighbor information around the pixels and optimize in an iterative manner, it is more efficient to capture multi-scale contextual information and directly tackles the original image. Additionally, DNINR turns to capture the pixel-level distribution information by means of wide and transformed network learning. In terms of edge preservation and noise suppression, the proposed DNINR consistently achieved significantly superior performance, which is better than current state-of-the-art methods, particularly at extremely high noise levels.

中文翻译：

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密集连接的网络可消除脉冲噪声

最近，一种被称为密集连接卷积网络（DenseNet）的新卷积神经网络（CNN）架构在图像分类任务上显示了出色的效果。DenseNet的想法基于以下观察结果：如果每一层都以前馈方式直接连接到其他每一层，则网络将更加准确且易于训练。在这项研究中，我们扩展了DenseNet来处理减少脉冲噪声的问题。它旨在探索用于脉冲噪声消除（DNINR）的密集连接网络，该网络利用CNN从嘈杂的图像中学习像素分布特征。与传统的基于中值滤波器的变分正则化方法相比，该方法利用像素周围的空间邻域信息并以迭代方式进行优化，捕获多尺度上下文信息并直接处理原始图像更加有效。此外，DNINR借助广泛且经过转换的网络学习来捕获像素级分布信息。在边缘保持和噪声抑制方面，建议的DNINR始终获得显着优越的性能，这比当前的最新技术要好，尤其是在极高的噪声水平下。