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Related Experiment Video

Updated: Oct 20, 2025

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

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Mobile Computational Photography: A Tour.

Mauricio Delbracio¹, Damien Kelly¹, Michael S Brown²

¹Google Research, Mountain View, California 94043, USA; email: mdelbra@google.com, damienkelly@google.com, milanfar@google.com.

Annual Review of Vision Science

|September 15, 2021

Summary

This summary is machine-generated.

Computational photography has transformed mobile phones into powerful cameras. Advances in algorithms, machine learning, and techniques like burst photography, noise reduction, and super-resolution drive this evolution.

Keywords:

burst processing camera pipeline computational photography low-light imaging mobile camera noise reduction super-resolution zoom

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Area of Science:

Computational photography
Mobile imaging technology
Computer vision

Background:

The evolution of mobile phones from basic communication devices to sophisticated imaging tools.
The historical context of early mobile camera phones and the shift towards advanced imaging capabilities.

Purpose of the Study:

To review the history and technological advancements in mobile computational photography.
To explain how computational photography has enhanced mobile camera capabilities.

Main Methods:

Review of historical developments in mobile camera technology.
Description of key computational photography techniques such as burst photography, noise reduction, and super-resolution.
Exploration of the role of machine learning and algorithmic advances.

Main Results:

Mobile phones have become primary imaging devices due to computational photography.
Key technologies like burst photography, noise reduction, and super-resolution significantly improve image quality.
Algorithmic and machine learning advancements have redefined mobile photography.

Conclusions:

Computational photography is the driving force behind the advanced imaging capabilities of modern smartphones.
The integration of sophisticated algorithms and machine learning has revolutionized image capture, processing, storage, and sharing.
Parallels can be drawn between computational photography techniques and the human visual system.