采用多通道浅层CNN构建的多降噪器最优组合模型

现有的一致性神经网络(Consensus neural network, CsNet)利用凸优化和神经网络技术将多个降噪算法(降噪器)输出的图像进行加权组合(融合), 以获得更好的降噪效果, 但该优化模型在降噪效果和执行效率方面仍有较大改进空间. 为此, 提出一种基于轻量型多通道浅层卷积神经网络(Multi-channel shallow convolutional neural network, MSCNN)构建的多降噪器最优组合(Optimal combination of image denoisers, OCID)模型. 该模型采用多通道输入结构直接接收由多个降噪器输出的降噪图像, 并利用残差学习技术合并完成图像融合和图像质量提升两项任务. 具体使用时, 对于给定的一张噪声图像, 先用多个降噪器对其降噪, 并将降噪后图像输入OCID模型获得残差图像, 然后将多个降噪图像的均值图像与残差图像相减, 所得到图像作为优化组合后的降噪图像. 实验结果表明, 与CsNet组合模型相比, 网络结构更为简单的OCID模型以更小的计算代价获得了图像质量更高的降噪图像.

Optimal Combination of Image Denoisers Using Multi-channel Shallow Convolutional Neural Network

The existing consensus neural network (CsNet) model using the convex optimization and the neural network techniques achieves an optimal combination of the outputs of multiple denoisers for better denoising effect. However, the optimization model still has much room for improvement in noise denoising effect and execution efficiency. To this end, a lightweight multi-channel shallow convolutional neural network (MSCNN)-based model for optimal combination of image denoisers (OCID) was proposed. The OCID model used a multi-channel input structure to receive the outputs of multiple denoisers, and adopted the residual training learning technique to accomplish the task of image combination and quality improvement. For a given noisy image, multiple denoisers were first used to preprocess the given noisy image, and the denoised images were simultaneously fed into the OCID model to obtain a residual image. Then the mean image of the denoised images was subtracted from the residual image to obtain the final optimal denoised image. The experimental results show that, compared with the CsNet model, the images obtained by the OCID model with simpler network structure achieve the better image quality at lower computational cost.