Paper
7 May 2003 Simultaneous luminance and position stabilization for film and video
Anil Christopher Kokaram, Rozen Dahyot, Francois Pitie, Hugh Denman
Author Affiliations +
Proceedings Volume 5022, Image and Video Communications and Processing 2003; (2003) https://doi.org/10.1117/12.476584
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States
Abstract
Temporal and spatial random variation of luminance in images, or 'flicker' is a typical degradation observed in archived film and video. The underlying premise in typical flicker reduction algorithms is that each image must be corrected for a spatially varying gain and offset. These parameters are estimated in the stationary region of the image. Hence the performance of that algorithm depends crucially on the identification of stationary image regions. Position fluctuations are also a common artefact resulting in a random 'shake' of each film frame. For removing both, the key is to reject regions showing local motion or other outlier activity. Parameters are then estimated mostly on that part of the image undergoing the dominant motion. A new algorithm that simultaneously deals with global motion estimation and flicker is presented. The final process is based on a robust application of weighted least-squares, in which the weights also classify portions of the image as local or global. The paper presents results on severely degraded sequences showing evidence of both Flicker and random shake.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Anil Christopher Kokaram, Rozen Dahyot, Francois Pitie, and Hugh Denman "Simultaneous luminance and position stabilization for film and video", Proc. SPIE 5022, Image and Video Communications and Processing 2003, (7 May 2003); https://doi.org/10.1117/12.476584
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CITATIONS
Cited by 22 scholarly publications and 5 patents.
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KEYWORDS
Motion estimation

Image processing

Video

Motion models

Visualization

Error analysis

Linear filtering

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