Deep learning (DL) is a field of study within machine learning and signal processing that has been around for nearly 40 years. In the last 10 years, its progress on problems including speech-to-text, image recognition, image generation, and language generation has been phenomenal. This exponential progress has been driven by exciting engineering and algorithmic developments.
Signal processing (SP), in its essence, aims to extract useful information out of noisy and incomplete physical measurements. Classically, one might exploit known mathematical models of these measured signals, e.g., harmonics of a musical instrument or physics of a medical imaging device. Modern deep learning (DL) shares a similar goal, namely, to extract useful information out of complex high-dimensional observations, but in contrast, it replaces known physical models with vast datasets, signaling a transition from model-based to data-based algorithms.
Hypercomplex signal and image processing extends upon conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. The special issue is divided into two parts and is focused on current advances and applications in computational signal and image processing in the hypercomplex domain.
It is our great pleasure to introduce the first part of this special issue to you! The IEEE Signal Processing Society (SPS) has completed 75 years of remarkable service to the signal processing community. When the Society was founded in 1948, we couldn’t imagine, for instance, how wireless networks of smartphones would be able to connect us easily at all times, or that an image processing algorithm would be able to detect cancer in a few seconds.
Thanks to the tremendous interest from the research community, the focus of the March issue of the IEEE Signal Processing Magazine is on the second volume of the special issue on physics-driven machine learning for computational imaging, which brings together nine articles of the 19 accepted papers from the original 47 submissions.
Recent years have witnessed a rapidly growing interest in next-generation imaging systems and their combination with machine learning. While model-based imaging schemes that incorporate physics-based forward models, noise models, and image priors laid the foundation in the emerging field of computational sensing and imaging, recent advances in machine learning, from large-scale optimization to building deep neural networks, are increasingly being applied in modern computational imaging.
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