Abstract
Although deep convolutional neural networks (CNNs) have received great attention in image denoising, most CNN-based methods do not take full account of the hierarchical features of the original low-quality images, including the spatial feature information within channels, which decreases the representational capacity of the network. To solve this problem, we propose a dense activation network (DAN) with a dense activation block (DAB) consisting of a dense information fusion (DIF) net and a spatial activation (SA) net. Specifically, DIF takes into account both local feature information in the current block and global feature information across blocks to enhance the propagation of feature information, and it adopts dilated convolution to enlarge the receptive field of a network. Furthermore, we focus on the intra-channel spatial relationships and propose a novel SA net that adaptively recalibrates features by considering the spatial relationships within channels. Experiments on benchmark datasets show that our DAN models achieve favorable performance against state-of-the-art methods.
This work has been supported in part by National Natural Science Foundation of China under Grants 61702032, 61573057, 61771042; by Fund 6140001030213; by Fund 2017JBZ002.
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Shen, Y., Zhang, L., Lou, S., Wang, Z. (2019). Dense Activation Network for Image Denoising. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_7
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