Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2018, Cilt: 8 Sayı: 2, 168 - 178, 29.12.2018
https://doi.org/10.36222/ejt.457053

Öz

Kaynakça

  • [1] A. Goel, V.P. Vishwakarma, . Fractional DCT and DWT hybridization based efficient feature extraction for gender classification, Pattern Recognition Letters 95 (2017) 8–13.
  • [2] Q. Zhang, Uniqueness guarantees for phase retrieval from discrete windowed fractional Fourier transform, Optik 158 (2018) 1491–1498.
  • [3] J. Strain, Fast Fourier transforms of piecewise polynomials, Journal of Computational Physics 373 (2018) 346–369.
  • [4] Avci E., Tuncer T., Ertam F., Çok katmanlı görüntü steganografi, 7. Uluslararası Bilgi Güvenliği ve Kriptoloji Konferansı, 2014.
  • [5] I. Perfilieva, Fuzzy transforms: Theory and applications, Fuzzy Sets and Systems 157 (2006) 993 – 1023.
  • [6] F.D. Martino, P. Hurtik, I Perfilieva, S. Sessa, A color image reduction based on fuzzy transforms, Information Sciences 266 (2014) 101–111.
  • [7] M. Manchanda, R. Sharma, Multifocus Image Fusion Based on Discrete Fuzzy Transform, IEEE WiSPNET 2017 conference, 2017, 775-779.
  • [8] R. Chandrasekharan, S. M, Fuzzy Transform for Contrast Enhancement of Nonuniform Illumination Images, Ieee signal processing letters, vol. 25, no. 6, june 2018.
  • [9] M. Manchanda, R. Sharma, An improved multimodal medical image fusion algorithm based on fuzzy transform, Journal of Visual Communication and Image Representation 51 (2018) 76–94.
  • [10] V. Gregori, S. Morillas, B. Roig, A. Sapena, Fuzzy averaging filter for impulse noise reduction in colour images with a correction step, Journal of Visual Communication and Image Representation 55 (2018) 518–528.
  • [11] F.D. Martino, V. Loia, S. Sessa, A segmentation method for images compressed by fuzzy transforms, Fuzzy Sets and Systems 161 (2010) 56 – 74.
  • [12] F.D. Martino, S. Sessa, Fragile watermarking tamper detection with images compressed by fuzzy transform, Information Sciences 195 (2012) 62–90.
  • [13] F.D. Martino, S. Sessa, Compression and decompression of images with discrete fuzzy transforms, Information Sciences 177 (2007) 2349–2362.
  • [14] J. Mockor, P. Hurtik, Lattice-valued F-transforms and similarity relations, Fuzzy Sets and Systems 342 (2018) 67–89.
  • [15] G. Beliakov, H. Bustince, D. Paternain, Image reductions using means of discrete product lattices, IEEE Trans. Image Process. 21 (3) (2012) 1070–1083.

TRIANGLE FUZZY TRANSFORM BASED AUTOMATIC NOISE AND COLOR IMAGE REDUCTION METHODS

Yıl 2018, Cilt: 8 Sayı: 2, 168 - 178, 29.12.2018
https://doi.org/10.36222/ejt.457053

Öz

Noise reduction and image reduction are very important
research area for image processing and computer vision. Many papers have been
proposed for noise and image reduction. In this paper, novel triangle fuzzy
sets transform (F-transform) is proposed for color image denoising and
reduction. The proposed methods consist of histogram extraction, threshold
points calculation, fuzzy sets construction and fuzzy tansformation phases. Firstly,
histogram of the image are extracted, maximum points of histogram are
calculated, and these points are considered as threshold points. Fuzzy sets are
created using threshold points. Then, F-transform is applied on the overlapping
and non-overlapping blocks of the images for image denoising and reduction
respectively. The main objective of the presented method are to remove random
noises of the images and color image reduction with satisfactory visual quality.
In order to evaluate triangle fuzzy sets based F-transform applications,
variable noise intensities and block sizes are used. Mean absolute error (MEA),
peaks signal noise-to-ratio (PSNR) and penalized function (PEN) are utilized
for obtaining numerical results. Numerical simulations and comprasions clearly
illustare that the proposed triangle F-transform is good transformation for
random noises removing and image reduction.

Kaynakça

  • [1] A. Goel, V.P. Vishwakarma, . Fractional DCT and DWT hybridization based efficient feature extraction for gender classification, Pattern Recognition Letters 95 (2017) 8–13.
  • [2] Q. Zhang, Uniqueness guarantees for phase retrieval from discrete windowed fractional Fourier transform, Optik 158 (2018) 1491–1498.
  • [3] J. Strain, Fast Fourier transforms of piecewise polynomials, Journal of Computational Physics 373 (2018) 346–369.
  • [4] Avci E., Tuncer T., Ertam F., Çok katmanlı görüntü steganografi, 7. Uluslararası Bilgi Güvenliği ve Kriptoloji Konferansı, 2014.
  • [5] I. Perfilieva, Fuzzy transforms: Theory and applications, Fuzzy Sets and Systems 157 (2006) 993 – 1023.
  • [6] F.D. Martino, P. Hurtik, I Perfilieva, S. Sessa, A color image reduction based on fuzzy transforms, Information Sciences 266 (2014) 101–111.
  • [7] M. Manchanda, R. Sharma, Multifocus Image Fusion Based on Discrete Fuzzy Transform, IEEE WiSPNET 2017 conference, 2017, 775-779.
  • [8] R. Chandrasekharan, S. M, Fuzzy Transform for Contrast Enhancement of Nonuniform Illumination Images, Ieee signal processing letters, vol. 25, no. 6, june 2018.
  • [9] M. Manchanda, R. Sharma, An improved multimodal medical image fusion algorithm based on fuzzy transform, Journal of Visual Communication and Image Representation 51 (2018) 76–94.
  • [10] V. Gregori, S. Morillas, B. Roig, A. Sapena, Fuzzy averaging filter for impulse noise reduction in colour images with a correction step, Journal of Visual Communication and Image Representation 55 (2018) 518–528.
  • [11] F.D. Martino, V. Loia, S. Sessa, A segmentation method for images compressed by fuzzy transforms, Fuzzy Sets and Systems 161 (2010) 56 – 74.
  • [12] F.D. Martino, S. Sessa, Fragile watermarking tamper detection with images compressed by fuzzy transform, Information Sciences 195 (2012) 62–90.
  • [13] F.D. Martino, S. Sessa, Compression and decompression of images with discrete fuzzy transforms, Information Sciences 177 (2007) 2349–2362.
  • [14] J. Mockor, P. Hurtik, Lattice-valued F-transforms and similarity relations, Fuzzy Sets and Systems 342 (2018) 67–89.
  • [15] G. Beliakov, H. Bustince, D. Paternain, Image reductions using means of discrete product lattices, IEEE Trans. Image Process. 21 (3) (2012) 1070–1083.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Araştırma Makalesi
Yazarlar

Türker Tuncer

Yayımlanma Tarihi 29 Aralık 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 8 Sayı: 2

Kaynak Göster

APA Tuncer, T. (2018). TRIANGLE FUZZY TRANSFORM BASED AUTOMATIC NOISE AND COLOR IMAGE REDUCTION METHODS. European Journal of Technique (EJT), 8(2), 168-178. https://doi.org/10.36222/ejt.457053

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