A Note on Machine Learning Approach for Computational Imaging
It provides an incremental overview for researchers in computational imaging, highlighting integration potentials without presenting new results.
This note reviews recent machine learning developments in computational imaging, comparing them to mathematical approaches and discussing how combining both can address new computational and theoretical challenges.
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.