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Frame by Frame Denoising of a Video Clip


Affiliations
1 Electronics and Communication Department, Karunya University, Coimbatore, India
     

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In this paper, an approach to the problem of simultaneously enhancing image sequences and tracking the objects of interest is described. In particular, it is demonstrated that, in dynamic settings, useful statistical reports can come both from some reasonable assumptions on the properties of the image to be enhanced as well as from the images that have already been observed before the current scene. Using such informations form the main contribution of the present paper which is the proposal of the dynamic denoising as a tool for simultaneously enhancing and tracking image sequences. The dynamic denoising is demonstrated on several examples of Synthetic Aperture Radar (SAR) imagery.

Keywords

Bayesian Estimation, Kalman Filtering, Predictive Tracking, Wavelet Denoising.
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  • Frame by Frame Denoising of a Video Clip

Abstract Views: 235  |  PDF Views: 1

Authors

Roopa Jayasingh
Electronics and Communication Department, Karunya University, Coimbatore, India
D. Justin David
Electronics and Communication Department, Karunya University, Coimbatore, India
Jasmine David
Electronics and Communication Department, Karunya University, Coimbatore, India

Abstract


In this paper, an approach to the problem of simultaneously enhancing image sequences and tracking the objects of interest is described. In particular, it is demonstrated that, in dynamic settings, useful statistical reports can come both from some reasonable assumptions on the properties of the image to be enhanced as well as from the images that have already been observed before the current scene. Using such informations form the main contribution of the present paper which is the proposal of the dynamic denoising as a tool for simultaneously enhancing and tracking image sequences. The dynamic denoising is demonstrated on several examples of Synthetic Aperture Radar (SAR) imagery.

Keywords


Bayesian Estimation, Kalman Filtering, Predictive Tracking, Wavelet Denoising.