Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection

• URL: http://arxiv.org/abs/2304.01620v1
• Date: Tue, 4 Apr 2023 08:21:46 GMT
• Title: Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection
• Authors: Wencong Wu, Shicheng Liao, Guannan Lv, Peng Liang, Yungang Zhang
• Abstract summary: We propose a novel dual convolutional blind denoising network with skip connection (DCBDNet) The proposed DCBDNet consists of a noise estimation network and a dual convolutional neural network (CNN)
• Score: 2.9689285167236603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, deep convolutional neural networks have shown fascinating performance in the field of image denoising. However, deeper network architectures are often accompanied with large numbers of model parameters, leading to high training cost and long inference time, which limits their application in practical denoising tasks. In this paper, we propose a novel dual convolutional blind denoising network with skip connection (DCBDNet), which is able to achieve a desirable balance between the denoising effect and network complexity. The proposed DCBDNet consists of a noise estimation network and a dual convolutional neural network (CNN). The noise estimation network is used to estimate the noise level map, which improves the flexibility of the proposed model. The dual CNN contains two branches: a u-shaped sub-network is designed for the upper branch, and the lower branch is composed of the dilated convolution layers. Skip connections between layers are utilized in both the upper and lower branches. The proposed DCBDNet was evaluated on several synthetic and real-world image denoising benchmark datasets. Experimental results have demonstrated that the proposed DCBDNet can effectively remove gaussian noise in a wide range of levels, spatially variant noise and real noise. With a simple model structure, our proposed DCBDNet still can obtain competitive denoising performance compared to the state-of-the-art image denoising models containing complex architectures. Namely, a favorable trade-off between denoising performance and model complexity is achieved. Codes are available at https://github.com/WenCongWu/DCBDNet.

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