Compression and reconstruction of flotation foam images based on generative adversarial networks

The froth flotation is a beneficiation method that utilizes the differences in the physical and chemical properties of the surface of mineral particles to achieve the effective separation of different minerals. Recently, with the development of image processing and computer technology, machine vision has been widely used in mineral froth flotation. However, due to the high pixels of the flotation froth image and the limited storage space of the industrial computer, it is difficult to save a large amount of image data at the industrial site. To this end, a flotation foam image compression and reconstruction method is presented in this work. First, a lossless froth image is collected from the flotation cell in a gold hydrometallurgical plant. Then, the JPEG image compression algorithm compresses the original images to one in dozens. Under this situation, low-quality and small-volume images can be stored in an industrial computer easily. To reconstruct the compressed image, the generative adversarial networks (GAN) is used in this work, and RRDB and VGG19 are employed as the generative and discriminative models of the network, respectively. The loss function of the model is the sum of three losses, which are L1 loss, perceptual loss, and adversarial loss. Three methods for testing the texture features of the froth image are selected to verify the efficiency of the proposed method. The results show that the reconstructed image can effectively restore the details of the bubbles, reflect the image features, and has less noise and high visual similarity with the original image. The reconstructed images generated by low-resolution images with different compression qualities mostly maintain a restoration degree of more than 90% compared with the original images in three texture features.