With the development of various kinds of sensors, daily life and social activities can be conveyed by different kinds of signals or images/videos. Hence, there is a massive influx of substantial signals and images to be analyzed quickly and accurately, which requires a high level of scientific programming. Many machine learning (ML) algorithms have been developed to automatically interpret different kinds of signals and images using various feature extraction methods and classification schemes. Extracting the proper features from the measured data using advanced scientific programming algorithms is a challenging task. Therefore, advanced scientific programming methods such as deep learning (DL)-based programming are widely used for signal and image processing.

However, the signals and images are diverse and rich, which may have different properties. As a result, there are no universal algorithms or tools to process them properly. Targeted manners and algorithms should be consistently developed based on advanced computational intelligence algorithms for signal and image processing. The advanced computational intelligence algorithms including DL techniques like convolution neural networks (CNN), long short-term memory (LSTM), autoencoder, deep generative models, and deep belief networks have been applied for big data efficiently. The application of such novel computational intelligence methods in signal and image processing makes accurate and fast interpretations.

This Special Issue aims to collate original research and review articles describing advances in this field.

Potential topics include but are not limited to the following:

• Computational intelligence and deep learning for signals and images
• Advanced computational intelligence for signal and image presentation
• Deep neural networks for signal and image processing
• Nature-based computational intelligence algorithms for signal and image processing
• Deep learning vs traditional machine learning comparative analysis of signals and images
• Reviews on various computational intelligence architectures for signals and images
• Computational intelligence in biomedical signal processing
• Computational intelligence for medical image processing
• Computational intelligence for big data
• Computational intelligence for real-time analysis of data from the economy, sports, fitness, etc.