1997
DOI: 10.1109/76.585932
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Fuzzy detection of edge-direction for video line doubling

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Cited by 53 publications
(33 citation statements)
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“…an objective measure. We compare the WIS with the following recently published well-known deinterlacing algorithms: ELA [4], EELA [5], DOI [6], NEDD [7], FDED [8], MADLSCD [9], LABI [10], LCID [11], and FDOI [12]. Tables 1 and 2 include the average PSNR results and computational CPU time for various test sequences, respectively.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…an objective measure. We compare the WIS with the following recently published well-known deinterlacing algorithms: ELA [4], EELA [5], DOI [6], NEDD [7], FDED [8], MADLSCD [9], LABI [10], LCID [11], and FDOI [12]. Tables 1 and 2 include the average PSNR results and computational CPU time for various test sequences, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…In particular, many spatial interpolation techniques have been proposed to improve the quality of the interpolated images by enhancing the edges and the overall image sharpness. Among them are the classical edge direction-based linear filters, including ELA [4], EELA [5], DOI [6], NEDD [7], FDED [8], MADLSCD [9], LABI [10], and LCID [11]. An alternative is to employ a non-linear and soft computing-based approach which includes fuzzy logic for image interpolation, namely FDOI [12].…”
Section: Introductionmentioning
confidence: 99%
“…From the results shown in Table IV, it is concluded that the Fuzzy-ELA 5+5 H2 algorithm clearly surpasses not only the majority of the recently reported algorithms (ELA 3+3 [2], ELA 5+5 [2], NEDI [14], EELA [3], NEDD [12], FDED [17], but also the algorithm reported by de Haan [26]), although, in terms of average value, it offers no significant improvements over three of them: MELA [13], FWW [20], and IFRA [21].…”
Section: B Comparison With State-of-the-art Edge-adaptive De-interlamentioning
confidence: 97%
“…Mancuso et al [16] used soft-decision fuzzy logic techniques to remove noise and de-interlace TV video signals. A fuzzy edge-direction detector was introduced by Michaud et al [17] to orient a conventional vertico-temporal filter for de-interlacing tasks. Van De Ville et al [18] used fuzzy inference rule bases to detect motion and to choose the most appropriate de-interlacing strategy according to the degree of motion detected.…”
Section: Fig 1 Illustrates the Advantages Of Edge-adaptive De-interlmentioning
confidence: 99%
“…However, the SFC has many stages and still being studied. For instance, SFC is adopted in high definition television, mobile phone internet and various formats of video [5][6][7]. Therefore, there are requires for conversion between diverse formats.…”
Section: Introductionmentioning
confidence: 99%