基于深度多尺度卷积稀疏编码的图像去噪算法

Image Denoising Algorithm Based on Deep Multi-scale Convolution Sparse Coding

针对图像去噪深度网络缺乏可解释性的问题,利用深度展开思想,提出了一种基于多尺度卷积稀疏编码的深度去噪网络(MSCSC-Net)。首先利用多尺度卷积字典,构建了多尺度卷积稀疏编码模型,能有效地刻画图像中的多尺度结构特征,然后将多尺度卷积稀疏编码模型的传统迭代优化解展开为深度神经网络架构,即MSCSC-Net,其中网络的每层对应优化解的每次迭代。因此,提出的深度网络中参数可以通过优化模型来准确定义,提高了网络的可解释性。此外,为了更加有效地保留原始图像中的结构信息,MSCSC-Net采用了一种改进的残差学习思想,将输入噪声图像与上一层的中间去噪结果进行加权,并作为下一层的输入图像,以进一步提高网络的去噪效果。在公开数据集上的实验结果表明,与现有典型的基于深度学习去噪算法相比,MSCSC-Net具有一定的竞争力。特别地,在CBSD68数据集上,噪声等级为75时,MSCSC-Net的平均PSNR指标和SSIM指标比FFDNet分别提高了0.77%和2.2%。

Aiming at the problem of lacking of interpretability of deep image denoising networks,this paper proposes a multi-scale convolutional sparse coding network(MSCSC-Net)for image denoising using the idea of deep unfolding.Firstly,a multi-scale convolutional sparse coding(MSCSC)model is developed by exploiting the multi-scale convolutional dictionary,which can effectively express the multi-scale structure of image.Then,the traditional iterative optimization solution for solving the MSCSC model is unfolded into a deep neural network,namely MSCSC-Net.Each layer of MSCSC-Net corresponds to each iteration of the optimization solution.Therefore,the parameters of MSCSC-Net can be accurately defined through the traditional optimization model,which improves the interpretability.In addition,in order to preserve the structural of original image,the proposed MSCSC-Net adopts a revised residual learning idea,in which the weighted results of input noisy image and intermediate denoised image of previous layer are used as the input of next layer.Such revised residual learning can improve denoising performance further.Experimental results on public datasets show that MSCSC-Net is competitive to existing typical deep learning-based algorithms.Speci-fically,for the CBSD68 dataset at noise level 75,MSCSC-Net obtains 0.77%and 2.2%improvements over FFDNet in terms of the average PSNR and SSIM,respectively.