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.