Detection of dirt impairments from archived film sequences: survey and evaluations

Jinchang Ren; Theodore Vlachos

doi:10.1117/1.3456633

1 June 2010 Detection of dirt impairments from archived film sequences: survey and evaluations

Jinchang Ren, Theodore Vlachos

Author Affiliations +

Optical Engineering, Vol. 49, Issue 6, 067005 (June 2010). https://doi.org/10.1117/1.3456633

Abstract

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Jinchang Ren and Theodore Vlachos "Detection of dirt impairments from archived film sequences: survey and evaluations," Optical Engineering 49(6), 067005 (1 June 2010). https://doi.org/10.1117/1.3456633

Published: 1 June 2010

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available