Removing camera shake in smartphones without hardware stabilization

doi:10.1017/CBO9781107360181.006

5 - Removing camera shake in smartphones without hardware stabilization

Published online by Cambridge University Press: 05 June 2014

Filip Sroubek and

Jan Flusser

Edited by

A. N. Rajagopalan and

Rama Chellappa

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Filip Sroubek: Affiliation:
Czech Academy of Sciences, Czech Republic
Jan Flusser: Affiliation:
Czech Academy of Sciences, Czech Republic
A. N. Rajagopalan: Affiliation:
Indian Institute of Technology, Madras
Rama Chellappa: Affiliation:
University of Maryland, College Park

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Summary

Introduction

Processing images has become an everyday practice in a wide range of applications in science and technology and we rely on our images with ever growing emphasis. Our understanding of the world is however limited by measuring devices that we use to acquire images. Inadequate measuring conditions together with technological limitations of the measuring devices result in acquired images that represent a degraded version of the “true” image.

Blur induced by camera motion is a frequent problem in photography – mainly when the light conditions are poor. As the exposure time increases, involuntary camera motion has a growing effect on the acquired image. Image stabilization (IS) devices that help to reduce the motion blur by moving the camera sensor in the opposite direction are becoming more common. However, such a hardware remedy has its limitations as it can only compensate for motion of a very small extent and speed. Deblurring the image offline using mathematical algorithms is usually the only choice we have to obtain a sharp image. Motion blur can be modeled by convolution and the deblurring process is called deconvolution, which is a well-known ill-posed problem. In general, the situation is even more complicated, since we usually have no or limited information about the blur shape.

We can divide the deconvolution methods into two categories: methods that estimate the blur and the sharp image directly from the acquired image (blind deconvolution), and methods that use information from other sensors to estimate the blur (semi-blind deconvolution).

Type: Chapter
Information: Motion Deblurring
Algorithms and Systems
, pp. 100 - 122

DOI: https://doi.org/10.1017/CBO9781107360181.006 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2014

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