Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.

The resting-state functional magnetic resonance imaging (rs-fMRI) is a pivotal tool that can reveal brain dysfunction in the computer-aided diagnosis of the autism spectrum disorder (ASD). However, the instability of data collection devices, complexity of pathogenesis, and ambiguity in the causes of the disease always introduce considerable uncertainty in identifying ASD using rs-fMRI. Due to the strong ability of Takagi–Sugeno–Kang fuzzy inference systems (TSK FISs) in handling the uncertainty of knowledge and expression, we build an ASD classification model based on TSK FISs and further propose a novel multicenter ASD classification method FCG-MTGS-TSK. Specifically, the correlation information of multiple imaging centers is considered by introducing multitask group sparse learning, and the features across multiple imaging centers are thus jointly selected. An augmented lagrange multiplier (ALM) method is further developed to find the optimal solution of the model. Compared with the other existing methods, the proposed method has the advantages of strong interpretability and high classification accuracy. The experimental results also identify the most discriminative functional connectivity in multicenter ASD classification.