Densely connected network for impulse noise removal

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.