Colour image denoising by eigenvector analysis of neighbourhood colour samples

[EN] Colour image smoothing is a challenging task because it is necessary to appropriately distinguish between noise and original structures, and to smooth noise conveniently. In addition, this processing must take into account the correlation among the image colour channels. In this paper, we introduce a novel colour image denoising method where each image pixel is processed according to an eigenvector analysis of a data matrix built from the pixel neighbourhood colour values. The aim of this eigenvector analysis is threefold: (i) to manage the local correlation among the colour image channels, (ii) to distinguish between flat and edge/textured regions and (iii) to determine the amount of needed smoothing. Comparisons with classical and recent methods show that the proposed approach is competitive and able to provide significative improvements.Latorre-Carmona, P.; Miñana, J.; Morillas, S. (2020). Colour image denoising by eigenvector analysis of neighbourhood colour samples. Signal Image and Video Processing. 14(3):483-490. https://doi.org/10.1007/s11760-019-01575-5S483490143Plataniotis, K.N., Venetsanopoulos, A.N.: Color Image Processing and Applications. Springer, Berlin (2000)Lukac, R., Smolka, B., Martin, K., Plataniotis, K.N., Venetsanopoulos, A.N.: Vector Filtering for Color Imaging. IEEE Signal Processing Magazine, Special Issue on Color Image Processing 22, 74–86 (2005)Lukac, R., Plataniotis, K.N.: A taxonomy of color image filtering and enhancement solutions. In: Hawkes, P.W. (ed.) Advances in Imaging and Electron Physics, vol. 140, pp. 187–264. Elsevier Acedemic Press, Amsterdam (2006)Buades, A., Coll, B., Morel, J.M.: Nonlocal image and movie denoising. Int. J. Comput. Vis. 76, 123–139 (2008)Tomasi, C., Manduchi, R.: Bilateral filter for gray and color images. In: Proceedings of IEEE International Conference Computer Vision, pp. 839–846 (1998)Elad, M.: On the origin of bilateral filter and ways to improve it. IEEE Trans. Image Process. 11, 1141–1151 (2002)Kao, W.C., Chen, Y.J.: Multistage bilateral noise filtering and edge detection for color image enhancement. IEEE Trans. Consum. Electron. 51, 1346–1351 (2005)Garnett, R., Huegerich, T., Chui, C., He, W.: A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14, 1747–1754 (2005)Morillas, S., Gregori, V., Sapena, A.: Fuzzy Bilateral Filtering for color images. Lecture Notes Comput. Sci. 4141, 138–145 (2006)Zhang, B., Allenbach, J.P.: Adaptive bilateral filter for sharpness enhancement and noise removal. IEEE Trans. Image Process. 17, 664–678 (2008)Kenney, C., Deng, Y., Manjunath, B.S., Hewer, G.: Peer group image enhancement. IEEE Trans. Image Process. 10, 326–334 (2001)Morillas, S., Gregori, V., Hervás, A.: Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images. IEEE Trans. Image Process. 18, 1452–1466 (2009)Plataniotis, K.N., Androutsos, D., Venetsanopoulos, A.N.: Adaptive fuzzy systems for multichannel signal processing. Proc. IEEE 87, 1601–1622 (1999)Schulte, S., De Witte, V., Kerre, E.E.: A fuzzy noise reduction method for colour images. IEEE Trans. Image Process. 16, 1425–1436 (2007)Shen, Y., Barner, K.: Fuzzy vector median-based surface smoothing. IEEE Trans. Vis. Comput. Graph. 10, 252–265 (2004)Lukac, R., Plataniotis, K.N., Smolka, B., Venetsanopoulos, A.N.: cDNA microarray image processing using fuzzy vector filtering framework. Fuzzy Sets Syst. 152, 17–35 (2005)Smolka, B.: On the new robust algorithm of noise reduction in color images. Comput. Graph. 27, 503–513 (2003)Van de Ville, D., Nachtegael, M., Van der Weken, D., Philips, W., Lemahieu, I., Kerre, E.E.: Noise reduction by fuzzy image filtering. IEEE Trans. Fuzzy Syst. 11, 429–436 (2003)Schulte, S., De Witte, V., Nachtegael, M., Van der Weken, D., Kerre, E.E.: Histogram-based fuzzy colour filter for image restoration. Image Vis. Comput. 25, 1377–1390 (2007)Nachtegael, M., Schulte, S., Van der Weken, D., De Witte, V., Kerre, E.E.: Gaussian noise reduction in grayscale images. Int. J. Intell. Syst. Technol. Appl. 1, 211–233 (2006)Schulte, S., De Witte, V., Nachtegael, M., Mélange, T., Kerre, E.E.: A new fuzzy additive noise reduction method. Lecture Notes Comput. Sci. 4633, 12–23 (2007)Morillas, S., Schulte, S., Mélange, T., Kerre, E.E., Gregori, V.: A soft-switching approach to improve visual quality of colour image smoothing filters. In: Proceedings of Advanced Concepts for Intelligent Vision Systems ACIVS07, Lecture Notes in Computer Science, vol. 4678, pp. 254–261 (2007)Lucchese, L., Mitra, S.K.: A new class of chromatic filters for color image processing: theory and applications. IEEE Trans. Image Process. 13, 534–548 (2004)Lee, J.A., Geets, X., Grégoire, V., Bol, A.: Edge-preserving filtering of images with low photon counts. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1014–1027 (2008)Russo, F.: Technique for image denoising based on adaptive piecewise linear filters and automatic parameter tuning. IEEE Trans. Instrum. Meas. 55, 1362–1367 (2006)Shao, M., Barner, K.E.: Optimization of partition-based weighted sum filters and their application to image denoising. IEEE Trans. Image Process. 15, 1900–1915 (2006)Ma, Z., Wu, H.R., Feng, D.: Partition based vector filtering technique for suppression of noise in digital color images. IEEE Trans. Image Process. 15, 2324–2342 (2006)Ma, Z., Wu, H.R., Feng, D.: Fuzzy vector partition filtering technique for color image restoration. Comput. Vis. Image Underst. 107, 26–37 (2007)Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990)Sroubek, F., Flusser, J.: Multichannel blind iterative image restoration. IEEE Trans. Image Process. 12, 1094–1106 (2003)Hu, J., Wang, Y., Shen, Y.: Noise reduction and edge detection via kernel anisotropic diffusion. Pattern Recognit. Lett. 29, 1496–1503 (2008)Li, X.: On modeling interchannel dependency for color image denoising. Int. J. Imaging Syst. Technol., Special issue on applied color image processing 17, 163–173 (2007)Keren, D., Gotlib, A.: Denoising color images using regularization and correlation terms. J. Vis. Commun. Image Represent. 9, 352–365 (1998)Lezoray, O., Elmoataz, A., Bougleux, S.: Graph regularization for color image processing. Comput. Vis. Image Underst. 107, 38–55 (2007)Elmoataz, A., Lezoray, O., Bougleux, S.: Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing. IEEE Trans. Image Process. 17, 1047–1060 (2008)Blomgren, P., Chan, T.: Color TV: total variation methods for restoration of vector-valued images. IEEE Trans. Image Process. 7, 304–309 (1998)Tschumperlé, D., Deriche, R.: Vector-valued image regularization with PDEs: a common framework from different applications. IEEE Trans. Pattern Anal. Mach. Intell. 27, 506–517 (2005)Plonka, G., Ma, J.: Nonlinear regularized reaction-diffusion filters for denoising of images with textures. IEEE Trans. Image Process. 17, 1283–1294 (2007)Melange, T., Zlokolica, V., Schulte, S., De Witte, V., Nachtegael, M., Pizurca, A., Kerre, E.E., Philips, W.: A new fuzzy motion and detail adaptive video filter. Lecture Notes Comput. Sci. 4678, 640–651 (2007)De Backer, S., Pizurica, A., Huysmans, B., Philips, W., Scheunders, P.: Denoising of multicomponent images using wavelet least-squares estimators. Image Vis. Comput. 26, 1038–1051 (2008)Dengwen, Z., Wengang, C.: Image denoising with an optimal threshold and neighboring window. Pattern Recognit. Lett. 29, 1694–1697 (2008)Schulte, S., Huysmans, B., Pizurica, A., Kerre, E.E., Philips, W.: A new fuzzy-based wavelet shrinkage image denoising technique. In: Proceedings of Advanced Concepts for Intelligent Vision Systems ACIVS06, Lecture Notes in Computer Science, vol. 4179, pp. 12–23 (2006)Pizurica, A., Philips, W.: Estimating the probability of the presence of a signal of interest in multiresolution single and multiband image denoising. IEEE Trans. Image Process. 15, 654–665 (2006)Scheunders, P.: Wavelet thresholding of multivalued images. IEEE Trans. Image Process. 13, 475–483 (2004)Sendur, L., Selesnick, I.W.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans. Signal Process. 50, 2744–2756 (2002)Balster, E.J., Zheng, Y.F., Ewing, R.L.: Feature-based wavelet shrinkage algorithm for image denoising. IEEE Trans. Image Process. 14, 2024–2039 (2005)Miller, M., Kingsbury, N.: Image denoising using derotated complex wavelet coefficients. IEEE Trans. Image Process. 17, 1500–1511 (2008)Zhang, B., Fadili, J.M., Starck, J.L.: Wavelets, ridgelets, and curvelets for poisson noise removal. IEEE Trans. Image Process. 17, 1093–1108 (2008)Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007)Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space. In: Proceedings of the IEEE International Conference on Image Processing ICIP2007 , pp. 313–316 (2007)Hao, B.B., Li, M., Feng, X.C.: Wavelet iterative regularization for image restoration with varying scale parameter. Signal Process. Image Commun. 23, 433–441 (2008)Zhao, W., Pope, A.: Image restoration under significat additive noise. IEEE Signal Process. Lett. 14, 401–404 (2007)Gijbels, I., Lambert, A., Qiu, P.: Edge-preserving image denoising and estimation of discontinuous surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1075–1087 (2006)Liu, C., Szeliski, R., Kang, S.B., Zitnik, C.L., Freeman, W.T.: Automatic estimation and removal of noise from a single image. IEEE Trans. Pattern Anal. Mach. Intell. 30, 299–314 (2008)Oja, E.: Principal components, minor components, and linear neural networks. Neural Netw. 5, 927–935 (1992)Takahashi, T.: Kurita, T.: Robust de-noising by kernel PCA. In: Proceedings of ICANN2002, Lecture Notes in Computer Science, vol. 2145, pp. 739–744 (2002)Park, H., Moon, Y.S.: Automatic denoising of 2D color face images using recursive PCA reconstruction. In: Proceedings of Advanced Concepts for Intelligent Vision Systems ACIVS06, Lecture Notes in Computer Science, vol. 4179, pp. 799–809 (2006)Teixeira, A.R., Tomé, A.M., Stadlthanner, K., Lang, E.W.: KPCA denoising and the pre-image problem revisited. Digital Signal Process. 18, 568–580 (2008)Astola, J., Haavisto, P., Neuvo, Y.: Vector median filters. Proc. IEEE 78, 678–689 (1990)Morillas, S., Gregori, V., Sapena, A.: Adaptive marginal median filter for colour images. Sensors 11, 3205–3213 (2011)Morillas, S., Gregori, V.: Robustifying vector median filter. Sensors 11, 8115–8126 (2011)Dillon, W.R., Goldstein, M.: Multivariate Analysis: Methods and Applications. Wiley, Hoboken (1984)Jackson, J.E.: A User’s Guide to Principal Components. Wiley, Hoboken (2003)Camacho, J., Picó, J.: Multi-phase principal component analysis for batch processes modelling. Chemom. Intell. Lab. Syst. 81, 127–136 (2006)Nomikos, P., MacGregor, J.: Multivariate SPC charts for monitoring batch processes. Technometrics 37, 41–59 (1995)Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)Grecova, Svetlana, Morillas, Samuel: Perceptual similarity between color images using fuzzy metrics. J. Vis. Commun. Image Represent. 34, 230–235 (2016)Fairchild, M.D., Johnson, G.M.: iCAM framework for image appearance differences and quality. J. Electron. Imaging 13(1), 126–138 (2004)Immerkaer, J.: Fast noise variance estimation. Comput. Vis. Image Underst. 64, 300–302 (1996

CoMoFoD #x2014; New database for copy-move forgery detection

Due to the availability of many sophisticated image processing tools, a digital image forgery is nowadays very often used. One of the common forgery method is a copy-move forgery, where part of an image is copied to another location in the same image with the aim of hiding or adding some image content. Numerous algorithms have been proposed for a copy-move forgery detection (CMFD), but there exist only few benchmarking databases for algorithms evaluation. We developed new database for a CMFD that consist of 260 forged image sets. Every image set includes forged image, two masks and original image. Images are grouped in 5 categories according to applied manipulation: translation, rotation, scaling, combination and distortion. Also, postprocessing methods, such as JPEG compression, blurring, noise adding, color reduction etc., are applied at all forged and original images. In this paper we present database organization and content, creation of forged images, postprocessing methods, and database testing. CoMoFoD database is available at http://www.vcl.fer.hr/comofodMinistry of Science, Education and Sport, China; project numbers: 036-0361630-1635 and 036-0361630-164