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In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mixed noise). For removing the mixed noise from the 1D/2D signal, weighted median filters are well known as a proper choice. We have also proposed a filtering tool based on the weighted median filter with a data-dependent method. We call this data-dependent weighted median (DDWM) filters. Nevertheless, the DDWM filter, its weights are controlled by some local information, is not enough performance to restore the image sequence degraded by the noise. The reason is that the DDWM filter is not able to obtain good filtering performance both in the still and moving regions of an image sequence. To overcome above drawback, we add motion information as a motion detector to the local information that controls the weights of the filters. This new filter is proposed as a Video-Data Dependent Weighted Median (Video-DDWM) filter. Through some simulations, the Video-DDWM filter is shown to give effective restoration results than that given by the DDWM filtering and the conventional filtering method with a motion-conpensation (MC).

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.2 pp.432-440

- Publication Date
- 2001/02/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- Special Section PAPER (Special Section on Noise Cancellation and Reduction Techniques)

- Category
- Noise Reduction for Image Signal

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Mitsuhiko MEGURO, Akira TAGUCHI, Nozomu HAMADA, "Data-Dependent Weighted Median Filtering with Robust Motion Information for Restoring Image Sequence Degraded by Additive Gaussian and Impulsive Noise" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 2, pp. 432-440, February 2001, doi: .

Abstract: In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mixed noise). For removing the mixed noise from the 1D/2D signal, weighted median filters are well known as a proper choice. We have also proposed a filtering tool based on the weighted median filter with a data-dependent method. We call this data-dependent weighted median (DDWM) filters. Nevertheless, the DDWM filter, its weights are controlled by some local information, is not enough performance to restore the image sequence degraded by the noise. The reason is that the DDWM filter is not able to obtain good filtering performance both in the still and moving regions of an image sequence. To overcome above drawback, we add motion information as a motion detector to the local information that controls the weights of the filters. This new filter is proposed as a Video-Data Dependent Weighted Median (Video-DDWM) filter. Through some simulations, the Video-DDWM filter is shown to give effective restoration results than that given by the DDWM filtering and the conventional filtering method with a motion-conpensation (MC).

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_2_432/_p

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@ARTICLE{e84-a_2_432,

author={Mitsuhiko MEGURO, Akira TAGUCHI, Nozomu HAMADA, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Data-Dependent Weighted Median Filtering with Robust Motion Information for Restoring Image Sequence Degraded by Additive Gaussian and Impulsive Noise},

year={2001},

volume={E84-A},

number={2},

pages={432-440},

abstract={In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mixed noise). For removing the mixed noise from the 1D/2D signal, weighted median filters are well known as a proper choice. We have also proposed a filtering tool based on the weighted median filter with a data-dependent method. We call this data-dependent weighted median (DDWM) filters. Nevertheless, the DDWM filter, its weights are controlled by some local information, is not enough performance to restore the image sequence degraded by the noise. The reason is that the DDWM filter is not able to obtain good filtering performance both in the still and moving regions of an image sequence. To overcome above drawback, we add motion information as a motion detector to the local information that controls the weights of the filters. This new filter is proposed as a Video-Data Dependent Weighted Median (Video-DDWM) filter. Through some simulations, the Video-DDWM filter is shown to give effective restoration results than that given by the DDWM filtering and the conventional filtering method with a motion-conpensation (MC).},

keywords={},

doi={},

ISSN={},

month={February},}

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TY - JOUR

TI - Data-Dependent Weighted Median Filtering with Robust Motion Information for Restoring Image Sequence Degraded by Additive Gaussian and Impulsive Noise

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 432

EP - 440

AU - Mitsuhiko MEGURO

AU - Akira TAGUCHI

AU - Nozomu HAMADA

PY - 2001

DO -

JO - IEICE TRANSACTIONS on Fundamentals

SN -

VL - E84-A

IS - 2

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - February 2001

AB - In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mixed noise). For removing the mixed noise from the 1D/2D signal, weighted median filters are well known as a proper choice. We have also proposed a filtering tool based on the weighted median filter with a data-dependent method. We call this data-dependent weighted median (DDWM) filters. Nevertheless, the DDWM filter, its weights are controlled by some local information, is not enough performance to restore the image sequence degraded by the noise. The reason is that the DDWM filter is not able to obtain good filtering performance both in the still and moving regions of an image sequence. To overcome above drawback, we add motion information as a motion detector to the local information that controls the weights of the filters. This new filter is proposed as a Video-Data Dependent Weighted Median (Video-DDWM) filter. Through some simulations, the Video-DDWM filter is shown to give effective restoration results than that given by the DDWM filtering and the conventional filtering method with a motion-conpensation (MC).

ER -