The Foundations of Computational Imaging: A signal processing perspective

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The Foundations of Computational Imaging: A signal processing perspective

W. Clem Karl; James E. Fowler; Charles A. Bouman; Müjdat Çetin; Brendt Wohlberg; Jong Chul Ye

Twenty-five years ago, the field of computational imaging arguably did not exist, at least not as a standalone arena of research activity and technical development. Of course, the idea of using computation to form images had been around for several decades, largely thanks to the development of medical imaging - such as magnetic resonance imaging (MRI) and X-ray tomography - in the 1970s and synthetic-aperture radar (SAR) even earlier. Yet, a quarter of a century ago, such technologies would have been considered to be a subfocus of the wider field of image processing. This view started to change, however, in the late 1990s with a series of innovations that established computational imaging as a scientific and technical pursuit in its own right.


In this article, we provide a signal processing perspective on the area of computational imaging, focusing on its emergence and evolution within the signal processing community. First, in the “Historical Perspective” section, we present an overview of the technical development of computational imaging wherein we trace the historical development of the field from its origins through to the present day. Then, in the “Signal Processing Society Involvement” section, we provide a brief overview of the involvement of the IEEE Signal Processing Society (SPS) in the field. Finally, in the “Conclusions” section, we make several concluding remarks.

Historical Perspective

We begin our discourse by tracing the history of computational image formation. We start this historical perspective with its origins in physics-dependent imaging and then proceed to model-based imaging, including the impact of priors and sparsity. We next progress to recent data-driven and learning-based image formations, finally coming full circle back to how physics-based models are being merged with big data and machine learning for improved outcomes.

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