Xiaojing Yang， Mengdi Sun，Mingzhu Zhang， Zhonggui Sun*
　

School of Mathematical Sciences, Liaocheng University

Abstract

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.


　

Paper

X. Yang, M. Sun, M. Zhang, Z. Sun*. Deep Convolutional Dictionary Learning for Multi-modal Image Denoising. accepted by 2022 The International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM 2022), 2022.

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