Department of Art, Tianjin Renai College, Tianjin 301636, China.
Department of Formative Convergence Arts, Hoseo University, Asan 31499, Republic of Korea.
Comput Intell Neurosci. 2022 Jun 1;2022:7145387. doi: 10.1155/2022/7145387. eCollection 2022.
Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved.
网络压缩编码技术是数字隐写术和图像合成领域的研究热点。如何在实现短压缩时间的同时提高图像质量是目前面临的问题。本文基于改进的小波神经网络理论，构建了一种数字隐写术和图像合成模型。该模型首先跟踪要识别的数字的轮廓，然后对轮廓进行均衡和重采样，使其具有平移不变性和缩放不变性，然后使用多小波神经网络聚类将轮廓壳拉伸到获得数量级多分辨率及其平均值，最后将这些壳系数输入前馈神经网络聚类来识别这个手写数字，解决了轮廓壳的多分辨率分解问题，同时具有较高的采样率。在模拟过程中，对原始灰度值及其相邻像素的灰度像素进行了单像素为文本/非文本像素的分类模型训练，然后通过自适应 MeanShift 方法将分类的文本像素连接到文本区域。实验结果表明，使用多小波特征对手写数字进行识别是可行的。该模型结合了神经网络和遗传算法，充分利用了两者的优势，使新算法具有神经网络的学习能力和鲁棒性。普通小波编码压缩后的压缩比、峰值信噪比、均方误差和压缩时间分别为 8.4、25dB、210 和 7s；分别为 11.7、24dB、207 和 11s。同时，峰值信噪比更高，均方误差更低，即压缩质量更好，有效提高了数字隐写术和图像合成的准确性。
