Alenezi Fayadh, Santosh K C
Department of Electrical Engineering, College of Engineering, Jouf University Sakaka, 72388, Saudi Arabia.
Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA.
Int J Biomed Imaging. 2021 Jan 22;2021:6664569. doi: 10.1155/2021/6664569. eCollection 2021.
One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.
霍普菲尔德神经网络（HNN）的一个主要缺点是该网络可能并不总是收敛到一个固定点。主要而言，HNN在训练期间限于局部优化以实现网络稳定性。在本文中，使用两种方法解决收敛问题：（a）基于通过像素梯度向量在各种图像超平面内的特征的几何相关性对连续改进的HNN（MHNN）的激活进行排序，以及（b）通过调节几何像素梯度向量。这些是通过在同调下对提出的MHNN进行正则化来实现的，这使它们能够充当像素谱序列的非常规滤波器。它将重点转移到局部和全局优化，以加强每个图像子空间内的特征相关性。结果，它增强了边缘、信息内容、对比度和分辨率。所提出的算法在十五种不同的医学图像上进行了测试，其中基于熵、视觉信息保真度（VIF）、加权峰值信噪比（WPSNR）、对比度和均匀性进行了评估。与四种现有的基准增强方法相比，我们的结果证实了其优越性。
