Xin Huamei, Sun Zengzhao, Xing Ying, Wang Jingjing
Appl Opt. 2021 Dec 10;60(35):10920-10927. doi: 10.1364/AO.442862.
Image denoising is a fundamental part of image processing. The real electronic speckle pattern interferometry (ESPI) contains a large amount of speckle noise, which affects the image quality and adversely affects subsequent studies. In this paper, a method based on an improved denoising convolutional neural network (CNN) has been proposed, with the goal of reducing noise while maintaining accurate information. The block matching 3D-based adaptive TV denoising CNN can protect the valid information while preventing the information of the original image itself from being corrupted. A two-channel model is used to improve the noise reduction effect of real images. The proposed method is compared with the conventional denoising algorithms and the deep-learning denoising algorithms. Experimental results show that the proposed method can maintain accuracy, integrity, and stability while preserving the details, texture, and edge information of the stripe pattern.
图像去噪是图像处理的一个基本部分。实际的电子散斑图案干涉术（ESPI）包含大量散斑噪声，这会影响图像质量并对后续研究产生不利影响。本文提出了一种基于改进的去噪卷积神经网络（CNN）的方法，目标是在保持准确信息的同时降低噪声。基于块匹配3D的自适应全变差去噪CNN可以在防止原始图像本身的信息被破坏的同时保护有效信息。使用双通道模型来提高真实图像的降噪效果。将所提出的方法与传统去噪算法和深度学习去噪算法进行了比较。实验结果表明，该方法在保留条纹图案的细节、纹理和边缘信息的同时，可以保持准确性、完整性和稳定性。
