XianWu Huang, Yuxiao Wang, ZhiHong Zhu, Haili Shang, Zhao Cao
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引用次数: 0
摘要
收集有关浮选泡沫特性的信息对于控制浮选生产条件非常重要。在煤泥浮选过程中获取的泡沫图像会受到环境光线等因素的影响,导致图像灰度不均匀,亮度和对比度较低。在使用网络模型从图像中提取特征信息时,通常需要增强泡沫图像的亮度。本文提出了一种基于多尺度卷积神经网络的泡沫图像亮度增强算法。该方法采用了基于对数函数求和连接设计的跳转连接结构,并在网络中引入了基于对数变换的损失函数。同时,在网络中设计了不同复杂度的分支网络,以进一步帮助缓解梯度消失问题。实验结果表明,在对泡沫图像和公共数据集 MIT 进行亮度增强后的图像质量评估时,在所提出的网络中使用所提出的跳转连接结构的数值结果总体上优于使用 resblock 结构的结果,所提出的损失函数也优于使用 L2 损失函数的结果。提出的网络大大改善了浮选泡沫图像的视觉效果,为浮选泡沫图像的特征提取和智能浮选生产奠定了基础。
Coal slurry foam image enhancement based on multiscale convolutional network
Collecting information on the flotation foam characteristics is important for controlling flotation production conditions. Foam images acquired during coal slurry flotation are affected by factors such as ambient lighting, contributing to uneven grayscale images with low brightness and contrast. Brightness enhancement of foam images is often required when using network models to extract feature information from the images. The paper proposes a foam image brightness enhancement algorithm based on a multiscale convolutional neural network. The method employs a skip connection structure based on a summation connection design based on logarithmic functions and introduces a loss function based on logarithmic transformation in the network. At the same time, branching networks of different complexity are designed in the network to further help alleviate the gradient vanishing problem. The experimental results show that when evaluating the quality of images after brightness enhancement of foam images and the public dataset MIT, the numerical results of using the proposed skip connection structure in the proposed network are overall better than using the resblock structure, and the proposed loss function is better than is better than using the L2 loss function. The proposed network greatly improves the visual effect of flotation foam images and lays the foundation for feature extraction of flotation foam images and intelligent flotation production.