Intelligent denoising technique for spatial video denoising for real-time applications

Mahmoud, Rasha; Faheem, Mohamed T.; Sarhan, Amany M.

doi:10.1109/icces.2008.4773037

Cited by 7 publications

(2 citation statements)

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“…Gijesh combined space-time vector of the wavelet coefficients and spatiotemporal Gaussian scale mixture to replicate the effect of motion [11]. Sezan proposed motion compensation based spatio-temporal linear minimum mean square error filter (LMMSE [14].…”

Section: Introductionmentioning

confidence: 99%

New approach for noise cleaning on real time videos based low-pass filtering and mathimatical morphology

Essaidani¹,

Seddik

Braiek

2013

2013 International Conference on Electrical Engineering and Software Applications

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Video de-noising presents a necessary step in video processing. It improves its quality and permits a preview processing for other applications such as objects tracking, compression, feature extraction, edge detection, motion tracking…etc. In this paper, we combined the mathematical morphology operations with low-pass filtering to improve real-time video de-noising corrupted by different noise types. Different kinds of morphologies and filters are proposed in order to define the best combination possible .This algorithm does not require any motion estimation. It isn't present a blur in de-noising video. Nevertheless, it can be a good reference for estimate motion in video.

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Section: Introductionmentioning

confidence: 99%

New approach for noise cleaning on real time videos based low-pass filtering and mathimatical morphology

Essaidani¹,

Seddik

Braiek

2013

2013 International Conference on Electrical Engineering and Software Applications

View full text Add to dashboard Cite

show abstract

“…Discrete wavelet denoising can be designed to eliminate noises presented in images so as to preserve the characteristics across all frequency ranges. The system is suitable for real-time applications (Mahmoud et al 2008). Using discrete wavelet transform, a digital image can be decomposed into an approximation component and detailed components (horizontal, vertical, diagonal).…”

Section: Introductionmentioning

confidence: 99%