计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 133-140.doi: 10.11896/jsjkx.220100090
尹海涛, 王天由
YIN Haitao, WANG Tianyou
摘要： 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%。
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