Two image enhancement contrast methods are proposed in this paper for low-intensity images. The first method (LEAM) is a new greyscale mapping function, and it can be significantly enhanced in the low grey range and compressed slowly in the high grey range, which is beneficial for retaining more image details; the second method (LEAAM) is based on the data characteristics of a histogram combined with the first mapping function, which adaptively sets the gamma value to correct the image. The experimental results show that compared with a traditional mapping function, LEAM is more effective at enriching image details and enhancing visual effects, and LEAAM, compared with a recent low-illumination image enhancement algorithm, achieves good performance for average gradient, information entropy and contrast index; additionally, the overall visual effect is the best compared with other methods.
本文基于OTN(Optical Transport Network;光传送网)技术提出了电力通信网络业务路由优化算法,通过仿真分析,结果表明此算法可优化电力通信网络既有OTN业务,实时有效均衡网络风险,提升OTN业务运行可靠性与稳定性;此算法的适应度函数优化设计,可促使现有网络业务路由分配的时候,更加趋向选取光信噪比参数值相对较高的光通道,所以通过此网络业务路由优化算法,OSNR(Optical Signal Noise Ratio;光信噪比)均值可显著提高;可实现网络业务路由优化,实现最佳风险均衡,且算法效率与收敛性较高,值得大力推广. 相似文献
Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
Machine Intelligence Research - One of the most significant challenges in the neuroscience community is to understand how the human brain works. Recent progress in neuroimaging techniques have... 相似文献
