Deep Convolutional Dictionary Learning for Multi-modal Image Denoising

With the development of neural networks, some learning-based image denoising methods have achieved powerful performance. However, they usually use single-modal information. So, their behaviors will be further improved if more believable (guided) modals can be introduced. In this work, we propose a novel architecture (MDCDicL) of deep neural network for multi-modal image denoising. Based on K-SVD, we give the constrained optimization learning model of MDCDicL. Then, with Half Quadratic Splitting, the model is unfolded into a deep convolution neural network. As expected, with the help of the guided modal, MDCDicL exhibits powerful performance. Its effectiveness is preliminarily verified on a standard flash/non-flash dataset.