Mobile Computational Photography: A Tour

TL;DR

This paper reviews the evolution of mobile computational photography, highlighting technological advances like burst photography, noise reduction, and super-resolution that have transformed smartphone cameras into powerful imaging tools.

Contribution

It provides a comprehensive overview of the history and key technological components of mobile computational photography, emphasizing recent innovations and their impact.

Findings

Advances in algorithms have significantly improved image quality.

Machine learning has enabled new modes of image capture and processing.

Mobile cameras now rival traditional cameras in quality and capabilities.

Abstract

The first mobile camera phone was sold only 20 years ago, when taking pictures with one's phone was an oddity, and sharing pictures online was unheard of. Today, the smartphone is more camera than phone. How did this happen? This transformation was enabled by advances in computational photography -the science and engineering of making great images from small form factor, mobile cameras. Modern algorithmic and computing advances, including machine learning, have changed the rules of photography, bringing to it new modes of capture, post-processing, storage, and sharing. In this paper, we give a brief history of mobile computational photography and describe some of the key technological components, including burst photography, noise reduction, and super-resolution. At each step, we may draw naive parallels to the human visual system.