A Note on Machine Learning Approach for Computational Imaging

TL;DR

This paper reviews recent machine learning methods in computational imaging, compares them with mathematical approaches, and discusses how combining both can enhance imaging techniques while highlighting new challenges.

Contribution

It provides a comparative analysis of machine learning and mathematical methods in computational imaging and explores the potential of their integration.

Findings

Machine learning has significantly advanced computational imaging.

Combining machine learning with mathematical approaches offers new potentials.

The integration introduces novel computational and theoretical challenges.

Abstract

Computational imaging has been playing a vital role in the development of natural sciences. Advances in sensory, information, and computer technologies have further extended the scope of influence of imaging, making digital images an essential component of our daily lives. For the past three decades, we have witnessed phenomenal developments of mathematical and machine learning methods in computational imaging. In this note, we will review some of the recent developments of the machine learning approach for computational imaging and discuss its differences and relations to the mathematical approach. We will demonstrate how we may combine the wisdom from both approaches, discuss the merits and potentials of such a combination and present some of the new computational and theoretical challenges it brings about.