基于BP神经网络的全变差模型参数选择

Parameter Selection of Total Variation Model Based on BP Neural Network

摘要: 基于变分理论提出的能量泛函极小化，在图像去噪中已经有了很广泛的应用。在去噪过程中，合理的选取参数是十分重要的，神经网络的通用性和可操作性比传统的算法有更多的优势。本文针对全变差去噪模型中不能自适应调整正则项参数的缺点，通过构建BP神经网络模型，经过大量的学习训练，模拟出图像原始信息和正则项参数的关系，根据模型得到的正则项参数，再结合Chambolle对偶算法，两者构成一个整体，这样改进后的算法其参数选取更为精确，去噪效果更加有效。

Abstract: The energy functional minimization proposed by the variational theory has been widely used in image denoising. In the process of denoising, it is very important to select parameters reasonably. The versatility and operability of neural networks have more advantages than traditional algo-rithms. In this paper, the shortcomings of the regular variable parameter cannot be adaptively adjusted in the total variation denoising model. By constructing the BP neural network model, after a lot of learning and training, the relationship between the original image information and the regular term parameters is simulated, and the regular term obtained from the model is obtained. The parameters, combined with the Chambolle dual algorithm, form a whole, so that the improved algorithm has more accurate parameter selection and more effective denoising effect.