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In recent years, deep convolutional neural networks have shown fascinatingperformance in the field of image denoising. However, deeper networkarchitectures are often accompanied with large numbers of model parameters,leading to high training cost and long inference time, which limits theirapplication in practical denoising tasks. In this paper, we propose a noveldual convolutional blind denoising network with skip connection (DCBDNet),which is able to achieve a desirable balance between the denoising effect andnetwork complexity. The proposed DCBDNet consists of a noise estimation networkand a dual convolutional neural network (CNN). The noise estimation network isused to estimate the noise level map, which improves the flexibility of theproposed model. The dual CNN contains two branches: a u-shaped sub-network isdesigned for the upper branch, and the lower branch is composed of the dilatedconvolution layers. Skip connections between layers are utilized in both theupper and lower branches. The proposed DCBDNet was evaluated on severalsynthetic and real-world image denoising benchmark datasets. Experimentalresults have demonstrated that the proposed DCBDNet can effectively removegaussian noise in a wide range of levels, spatially variant noise and realnoise. With a simple model structure, our proposed DCBDNet still can obtaincompetitive denoising performance compared to the state-of-the-art imagedenoising models containing complex architectures. Namely, a favorabletrade-off between denoising performance and model complexity is achieved. Codesare available at https://github.com/WenCongWu/DCBDNet.

Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection