Abstract In model-driven image denoising, prior regularization terms are required to be constructed in advance, resulting in high computational cost of dealing with optimization models. Data-driven methods possess superior performance and high efficiency due to the flexible architecture and powerful learning capability of neural networks, but their interpretability is insufficient. Therefore, truncated nuclear norm based unfolding network for image denoising is proposed and combined with the model-driven method based on truncated nuclear norm and image denoising in low-rank matrix recovery. Each iteration is regarded as a stage of the unfolding network. Singular value operators are learned with the help of neural networks to solve the problem of expensive computation of singular value decomposition in traditional iterative algorithms. Each of the stages is connected to form an end-to-end trainable unfolding network. The effectiveness of the proposed network is verified by the experiments on multiple datasets of image denoising.
Fund:National Natural Science Foundation of China(No.62176244,62006215), Fundamental Research Funds for the Provincial Universities of Zhejiang(No.2022YW65)
Corresponding Authors:
CAO Feilong, Ph.D., professor. His research interests include deep learning and image processing.
About author:: ZHANG Lin, master student. Her research interests include deep learning and image processing.YE Hailiang, Ph.D., lecturer. His research interests include deep learning and image processing.YANG Bing, Ph.D., lecturer. His research interests include deep learning and image processing.
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2022, Vol. 35 
