Editorial: Recent advances in artificial neural networks and embedded systems for multi-source image fusion

[...]studies of image fusion can be divided into two areas: first, new end-to-end neural network models for merging constituent parts during the image fusion process; second, the embodiment of artificial neural networks for image fusion systems. In this method, the cluster number K was calculated by fusing local binary patterns and gray-level co-occurrence matrix characteristic values. [...]the clustering and segmentation operation can be performed on Tujia brocade images by adopting a Gaussian mixture model to get a rough preliminary segmentation image. In the fourth paper, Wu et al. propose fractional wavelet-based generative scattering networks (FrScatNets) in which fractional wavelet scattering networks are used as the encoder to extract image features, with deconvolutional neural networks acting as the decoder, to generate an image. [...]the authors also developed a feature-map fusion method to reduce the dimensionality of FrScatNet embeddings. [...]wavelet decomposed multiscale magnitude spectra for every single channel were produced.