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0.1.15.1.2011.04.ARO.Fuzzy Random Impulse Noise Removal From Color Image Sequences.TIP

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0.1.15.1.2011.04.ARO.Fuzzy Random Impulse Noise Removal From Color Image Sequences.TIP Powered By Docstoc
					IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 4, APRIL 2011                                                                                     959




                   Fuzzy Random Impulse Noise Removal
                        From Color Image Sequences
                                           Tom Mélange, Mike Nachtegael, and Etienne E. Kerre


   Abstract—In this paper, a new fuzzy filter for the removal of                     which allows a more gradual transition between belonging to
random impulse noise in color video is presented. By working with                   and not belonging to. Such gradual transition makes fuzzy sets
different successive filtering steps, a very good tradeoff between                   very useful for the processing of human knowledge in terms of
detail preservation and noise removal is obtained. One strong fil-
tering step that should remove all noise at once would inevitably                   linguistic variables (e.g., large, small, etc.). There is for example
also remove a considerable amount of detail. Therefore, the noise                   no need to use a threshold to decide whether a difference in color
is filtered step by step. In each step, noisy pixels are detected by                 component value between two pixels is large or not. Two differ-
the help of fuzzy rules, which are very useful for the processing of                ences that differ only one unit (which is not noticeable by the
human knowledge where linguistic variables are used. Pixels that
are detected as noisy are filtered, the others remain unchanged. Fil-
                                                                                    human eye) could then be respectively large and not large. It is
tering of detected pixels is done by blockmatching based on a noise                 better to allow a difference to be large to some intermediate de-
adaptive mean absolute difference. The experiments show that the                    gree. For a larger difference, this degree will be higher than that
proposed method outperforms other state-of-the-art filters both vi-                  of a smaller difference. For an illustration of the effectiveness
sually and in terms of objective quality measures such as the mean                  of fuzzy set theory in image processing, we refer to, e.g., [25].
absolute error (MAE), the peak-signal-to-noise ratio (PSNR) and
the normalized color difference (NCD).                                                 Most filters in literature, that are developed for video, are
                                                                                    intended for sequences corrupted by additive Gaussian noise
   Index Terms—Circuits and systems, computers and information
processing, computational and artificial intelligence, filtering, fil-
                                                                                    (e.g., [3]–[7]). Only few video filters for the impulse noise case
ters, fuzzy logic, image denoising, logic, nonlinear filters.                        can be found (e.g., [19]–[23], [30], [31], [45], [46]). However,
                                                                                    several impulse noise filters for still images exist. The best
                                                                                    known among them are the median based rank-order filters
                            I. INTRODUCTION                                         (e.g., [8]–[10], [32], [34]–[40], [48]. But also some fuzzy tech-
                                                                                    niques can be found [11]–[18], [33], [41]. Such 2-D filters could
     MAGES and videos belong to the most important infor-
I    mation carriers in today’s world (e.g., traffic observations,
surveillance systems, autonomous navigation, etc.). However,
                                                                                    be used to filter each of the frames of a video successively.
                                                                                    However, temporal inconsistencies will arise due to the neglec-
                                                                                    tion of the temporal correlation between successive frames.
the images are likely to be corrupted by noise due to bad acquisi-
                                                                                    A better alternative would be to use 3-D filtering windows,
tion, transmission or recording. Such degradation negatively in-
                                                                                    in which also pixels from neighboring frames are taken into
fluences the performance of many image processing techniques
                                                                                    account [19]–[23], [30], [31], [45], [46]. The main problem in
and a preprocessing module to filter the images is often required.
                                                                                    using neighboring frames is motion between them. Using pixels
   Among those filters, more and more fuzzy techniques start
                                                                                    at corresponding spatial positions in neighboring frames for
to appear in literature [7], [11]–[18], [33], [41], [43], [44]–[47],
                                                                                    noise removal may introduce ghosting artifacts in the presence
[49], [52], [53]. Fuzzy set theory was introduced by Zadeh in
                                                                                    of camera and object motion. In the method proposed in this
1965 [24] and is a generalization of classical set theory. A clas-
                                                                                    paper, we will therefore only in non-moving areas assign a
sical crisp set over a universe can be modelled by a
                                                                                    temporal impulse between two corresponding spatial positions
mapping (characteristic function): an element             belongs to
                                                                                    to noise (detection phase) and for the replacement of a noisy
the set or does not belong to it. Fuzzy sets are now modelled as
                                                                                    pixel (filtering phase) motion compensation will be applied to
            mappings (membership functions). So the character-
                                                                                    find the most reliable pixel in the previous frame.
istic function is extended to a membership function where also
                                                                                       Analogously, a distinction between filters intended for
membership degrees between zero and one are allowed. An el-
                                                                                    greyscale images and for color images needs to be made. Filters
ement            can now also belong to some degree to the set,
                                                                                    for greyscale images could be used for color images by applying
                                                                                    them on each of the color bands of the image separately. In this
   Manuscript received March 25, 2010; revised July 12, 2010; accepted August       paper, we consider the images to be modeled in the RGB color
31, 2010. Date of publication September 20, 2010; date of current version March
18, 2011. This work was supported by the GOA project B/04138/01 of Ghent            space and we thus have three color bands: red, green and blue.
University. The associate editor coordinating the review of this manuscript and     However, such approach will generally introduce many color
approving it for publication was Dr. Kenneth K. M. Lam.                             artefacts, especially in textured areas, due to the neglection of
   The authors are with the Fuzziness and Uncertainty Modeling Research Unit,
Department of Applied Mathematics and Computer Science, Ghent Univer-               the correlation between the different color bands. To incorpo-
sity, 9000 Ghent, Belgium (e-mail: tom.melange@ugent.be; mike.nachtegael            rate this correlation, vector-based methods were introduced.
@ugent.be; etienne.kerre@ugent.be; http://www.fuzzy.ugent.be).                      Most of these methods are based on ordering the vectors in a
   Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.                                                      predefined filtering window. The output for a given color pixel
   Digital Object Identifier 10.1109/TIP.2010.2077305                                is then the pixel in the window around the given pixel, that has
                                                                 1057-7149/$26.00 © 2011 IEEE
MÉLANGE et al.: FUZZY RANDOM IMPULSE NOISE REMOVAL FROM COLOR IMAGE SEQUENCES                                                                                 969



                                                                                  is based on fuzzy rules in which information from spatial and
                                                                                  temporal neighbors as well as from the other color bands is used.
                                                                                  Detected noisy components are filtered based on blockmatching
                                                                                  where a noise adaptive mean absolute difference is used and
                                                                                  where the search region contains pixels blocks from both the
                                                                                  previous and current frame.
                                                                                     The experiments showed that the proposed method outper-
                                                                                  forms other state-of-the-art methods both in terms of objective
                                                                                  measures such as MAE, PSNR and NCD and visually.

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           directional (FD) filter to remove impulse noise from colour images,”          and intuitionistic fuzzy relations, fuzzy topology, and fuzzy image processing.
           IEICE Trans. Fundament. Electron., Commun. Comput. Sci., vol.                He has authored or co-authored 25 books, and more than 450 papers in interna-
           E93-A, no. 2, pp. 570–572, 2010.                                             tional refereed journals and proceedings.

				
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