# Classification of Mineral Foam Flotation Conditions Based on Multi-Modality Image Fusion
###### Abstract  
Accurate and rapid identification of mineral foam flotation states can increase mineral utilization and reduce the consumption of reagents. The traditional flotation process concentrates on extracting foam features from a single-modality foam image, and the accuracy is undesirable once problems such as insufficient image clarity or poor foam boundaries are encountered. In this work, a classification method based on multi-modality image fusion and CNN-PCA-SVM is proposed for work condition recognition of visible and infrared gray foam images. Specifically, the visible and infrared gray images are fused in the non-subsampled shearlet transform (NSST) domain using the parameter adaptive pulse coupled neural network (PAPCNN) method and the image quality detection method for high and low frequencies, respectively. The convolution neural network (CNN) is used as a trainable feature extractor to process the fused foam images, the principal component analysis (PCA) reduces feature data, and the support vector machine (SVM) is used as a recognizer to classify the foam flotation condition. After experiments, this model can fuse the foam images and recognize the flotation condition classification with high accuracy. multi-modal mineral foam image fusion; NSST; PAPCNN; image quality detection; CNN-PCA-SVM +
Footnote †: journal: Medical Electronics & Information Engineering  
**Citation:** [PERSON]; [PERSON]; [PERSON] Classification of Mineral Foam Flotation Conditions Based on Multi-Modality Image Fusion. _Applied_Sci. **2023**, _13_, 3512. [[https://doi.org/10.3390/app13063512](https://doi.org/10.3390/app13063512)]([https://doi.org/10.3390/app13063512](https://doi.org/10.3390/app13063512))  
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Footnote 1: email: jiang.x.zhuo.jzhuo. applications of infrared and visible image fusion. Therefore, in this paper, infrared and visible image fusion techniques are used to process foam images. In the past few decades, multiscale transformation (MST) has achieved success in areas such as infrared and visible image fusion and is the most active area in image fusion [4]. Generally, MST-based fusion methods include three basic steps. Firstly, the source image is decomposed to obtain low and high-frequency information. Secondly, the low-frequency and high-frequency information is fused according to the corresponding fusion rules. Finally, the fused high and low-frequency bands are used to perform the corresponding inverse transform to obtain the final fused image. The commonly used MST methods in image fusion include Laplace Pyramid (LP) [12], Wavelet Transform [13], Contourlet Transform [14], Non-subsampled Contourlet Transform (NSCT) [15], and Non-subsampled Shearlet Transform (NSST) [16], among which NSST has a great advantage of no direction number limitation and avoiding the Pseudo-Gibbs effect. In addition to the image transform selection, the high and low-frequency coefficient fusion strategy is also a key issue. The high frequency is usually chosen to fuse the maximum absolute value and the simple rule of sampling the weighted average of the low frequency to obtain the fusion coefficients [12]. Since most of the image energy is in the low-frequency sub-bands, the average fusion will reduce the contrast and lose the energy information of the source image. In this paper, the pulse-coupled neural network algorithm (PCNN) is used for high-frequency fusion, and the PCNN is a biological neural network derived from the cortical model of [PERSON] et al. [17], which has features such as global coupling and pulse synchronization and is well suited as a tool for image fusion. Specifically, PCNN is usually used to extract the activity level of the decomposition coefficients of the MST. In most cases, the parameters of PCNNs are empirically or manually entered, which largely limits the performance of the algorithm [18], and parametric adaptive PCNN (PAPCNN) [19] is introduced into the field of foam image fusion to overcome the difficulty of setting free parameters.