764 research outputs found

    Video denoising by fuzzy motion and detail adaptive averaging

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    A new fuzzy-rule-based algorithm for the denoising of video sequences corrupted with additive Gaussian noise is presented. The proposed method constitutes a fuzzy-logic-based improvement of a recent detail and motion adaptive multiple class averaging filter (MCA). The method is first explained in the pixel domain for grayscale sequences, and is later extended to the wavelet domain and to color sequences. Experimental results show that the noise in digital image sequences is efficiently removed by the proposed fuzzy motion and detail adaptive video filter (FMDAF), and that the method outperforms other state of the art filters of comparable complexity on different video sequences

    Fuzzy techniques for noise removal in image sequences and interval-valued fuzzy mathematical morphology

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    Image sequences play an important role in today's world. They provide us a lot of information. Videos are for example used for traffic observations, surveillance systems, autonomous navigation and so on. Due to bad acquisition, transmission or recording, the sequences are however usually corrupted by noise, which hampers the functioning of many image processing techniques. A preprocessing module to filter the images often becomes necessary. After an introduction to fuzzy set theory and image processing, in the first main part of the thesis, several fuzzy logic based video filters are proposed: one filter for grayscale video sequences corrupted by additive Gaussian noise and two color extensions of it and two grayscale filters and one color filter for sequences affected by the random valued impulse noise type. In the second main part of the thesis, interval-valued fuzzy mathematical morphology is studied. Mathematical morphology is a theory intended for the analysis of spatial structures that has found application in e.g. edge detection, object recognition, pattern recognition, image segmentation, image magnification… In the thesis, an overview is given of the evolution from binary mathematical morphology over the different grayscale morphology theories to interval-valued fuzzy mathematical morphology and the interval-valued image model. Additionally, the basic properties of the interval-valued fuzzy morphological operators are investigated. Next, also the decomposition of the interval-valued fuzzy morphological operators is investigated. We investigate the relationship between the cut of the result of such operator applied on an interval-valued image and structuring element and the result of the corresponding binary operator applied on the cut of the image and structuring element. These results are first of all interesting because they provide a link between interval-valued fuzzy mathematical morphology and binary mathematical morphology, but such conversion into binary operators also reduces the computation. Finally, also the reverse problem is tackled, i.e., the construction of interval-valued morphological operators from the binary ones. Using the results from a more general study in which the construction of an interval-valued fuzzy set from a nested family of crisp sets is constructed, increasing binary operators (e.g. the binary dilation) are extended to interval-valued fuzzy operators

    Fuzzy logic-based approach to wavelet denoising of 3D images produced by time-of-flight cameras

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    In this paper we present a new denoising method for the depth images of a 3D imaging sensor, based on the time-of-flight principle. We propose novel ways to use luminance-like information produced by a time-of flight camera along with depth images. Firstly, we propose a wavelet-based method for estimating the noise level in depth images, using luminance information. The underlying idea is that luminance carries information about the power of the optical signal reflected from the scene and is hence related to the signal-to-noise ratio for every pixel within the depth image. In this way, we can efficiently solve the difficult problem of estimating the non-stationary noise within the depth images. Secondly, we use luminance information to better restore object boundaries masked with noise in the depth images. Information from luminance images is introduced into the estimation formula through the use of fuzzy membership functions. In particular, we take the correlation between the measured depth and luminance into account, and the fact that edges (object boundaries) present in the depth image are likely to occur in the luminance image as well. The results on real 3D images show a significant improvement over the state-of-the-art in the field. (C) 2010 Optical Society of Americ

    Colour image denoising by eigenvector analysis of neighbourhood colour samples

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    [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

    A model based on local graphs for colour images and its application for Gaussian noise smoothing

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    [EN] In this paper, a new model for processing colour images is presented. A graph is built for each image pixel taking into account some constraints on links. Each pixel is characterized depending on the features of its related graph, which allows to process it appropriately. As an example, we provide a characterization of each pixel based on the link cardinality of its connected component. This feature enables us to properly distinguish flat image regions respect to edge and detail regions. According to this, we have designed a hybrid filter for colour image smoothing. It combines a filter able to properly process flat image regions with another one that is more appropriate for details and texture. Experimental results show that our model performs appropriately. We also see that our proposed filter is competitive with respect to state-of-the-art methods. It is close closer to the corresponding optimal switching filter respect to other analogous hybrid method.Samuel Morillas acknowledges the support of grant MTM2015-64373-P (MINECO/FEDER, UE). Cristina Jordan acknowledges the support of grant TEC2016-79884-C2-2-R.Pérez-Benito, C.; Morillas, S.; Jordan-Lluch, C.; Conejero, JA. (2018). A model based on local graphs for colour images and its application for Gaussian noise smoothing. Journal of Computational and Applied Mathematics. 330:955-964. https://doi.org/10.1016/j.cam.2017.05.013S95596433

    Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

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    The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level

    Color Image Processing based on Graph Theory

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    [ES] La visión artificial es uno de los campos en mayor crecimiento en la actualidad que, junto con otras tecnologías como la Biometría o el Big Data, se ha convertido en el foco de interés de numerosas investigaciones y es considerada como una de las tecnologías del futuro. Este amplio campo abarca diversos métodos entre los que se encuentra el procesamiento y análisis de imágenes digitales. El éxito del análisis de imágenes y otras tareas de procesamiento de alto nivel, como pueden ser el reconocimiento de patrones o la visión 3D, dependerá en gran medida de la buena calidad de las imágenes de partida. Hoy en día existen multitud de factores que dañan las imágenes dificultando la obtención de imágenes de calidad óptima, esto ha convertido el (pre-) procesamiento digital de imágenes en un paso fundamental previo a la aplicación de cualquier otra tarea de procesado. Los factores más comunes son el ruido y las malas condiciones de adquisición: los artefactos provocados por el ruido dificultan la interpretación adecuada de la imagen y la adquisición en condiciones de iluminación o exposición deficientes, como escenas dinámicas, causan pérdida de información de la imagen que puede ser clave para ciertas tareas de procesamiento. Los pasos de (pre-)procesamiento de imágenes conocidos como suavizado y realce se aplican comúnmente para solventar estos problemas: El suavizado tiene por objeto reducir el ruido mientras que el realce se centra en mejorar o recuperar la información imprecisa o dañada. Con estos métodos conseguimos reparar información de los detalles y bordes de la imagen con una nitidez insuficiente o un contenido borroso que impide el (post-)procesamiento óptimo de la imagen. Existen numerosos métodos que suavizan el ruido de una imagen, sin embargo, en muchos casos el proceso de filtrado provoca emborronamiento en los bordes y detalles de la imagen. De igual manera podemos encontrar una enorme cantidad de técnicas de realce que intentan combatir las pérdidas de información, sin embargo, estas técnicas no contemplan la existencia de ruido en la imagen que procesan: ante una imagen ruidosa, cualquier técnica de realce provocará también un aumento del ruido. Aunque la idea intuitiva para solucionar este último caso será el previo filtrado y posterior realce, este enfoque ha demostrado no ser óptimo: el filtrado podrá eliminar información que, a su vez, podría no ser recuperable en el siguiente paso de realce. En la presente tesis doctoral se propone un modelo basado en teoría de grafos para el procesamiento de imágenes en color. En este modelo, se construye un grafo para cada píxel de tal manera que sus propiedades permiten caracterizar y clasificar dicho pixel. Como veremos, el modelo propuesto es robusto y capaz de adaptarse a una gran variedad de aplicaciones. En particular, aplicamos el modelo para crear nuevas soluciones a los dos problemas fundamentales del procesamiento de imágenes: suavizado y realce. Se ha estudiado el modelo en profundidad en función del umbral, parámetro clave que asegura la correcta clasificación de los píxeles de la imagen. Además, también se han estudiado las posibles características y posibilidades del modelo que nos han permitido sacarle el máximo partido en cada una de las posibles aplicaciones. Basado en este modelo se ha diseñado un filtro adaptativo capaz de eliminar ruido gaussiano de una imagen sin difuminar los bordes ni perder información de los detalles. Además, también ha permitido desarrollar un método capaz de realzar los bordes y detalles de una imagen al mismo tiempo que se suaviza el ruido presente en la misma. Esta aplicación simultánea consigue combinar dos operaciones opuestas por definición y superar así los inconvenientes presentados por el enfoque en dos etapas.[CA] La visió artificial és un dels camps en major creixement en l'actualitat que, junt amb altres tecnlogies com la Biometria o el Big Data, s'ha convertit en el focus d'interés de nombroses investigacions i és considerada com una de les tecnologies del futur. Aquest ampli camp comprén diversos m`etodes entre els quals es troba el processament digital d'imatges i anàlisis d'imatges digitals. L'èxit de l'anàlisis d'imatges i altres tasques de processament d'alt nivell, com poden ser el reconeixement de patrons o la visió 3D, dependrà en gran manera de la bona qualitat de les imatges de partida. Avui dia existeixen multitud de factors que danyen les imatges dificultant l'obtenció d'imatges de qualitat òptima, açò ha convertit el (pre-) processament digital d'imatges en un pas fonamental previa la l'aplicació de qualsevol altra tasca de processament. Els factors més comuns són el soroll i les males condicions d'adquisició: els artefactes provocats pel soroll dificulten la inter- pretació adequada de la imatge i l'adquisició en condicions d'il·luminació o exposició deficients, com a escenes dinàmiques, causen pèrdua d'informació de la imatge que pot ser clau per a certes tasques de processament. Els passos de (pre-) processament d'imatges coneguts com suavitzat i realç s'apliquen comunament per a resoldre aquests problemes: El suavitzat té com a objecte reduir el soroll mentres que el real se centra a millorar o recuperar la informació imprecisa o danyada. Amb aquests mètodes aconseguim reparar informació dels detalls i bords de la imatge amb una nitidesa insuficient o un contingut borrós que impedeix el (post-)processament òptim de la imatge. Existeixen nombrosos mètodes que suavitzen el soroll d'una imatge, no obstant això, en molts casos el procés de filtrat provoca emborronamiento en els bords i detalls de la imatge. De la mateixa manera podem trobar una enorme quantitat de tècniques de realç que intenten combatre les pèrdues d'informació, no obstant això, aquestes tècniques no contemplen l'existència de soroll en la imatge que processen: davant d'una image sorollosa, qualsevol tècnica de realç provocarà també un augment del soroll. Encara que la idea intuïtiva per a solucionar aquest últim cas seria el previ filtrat i posterior realç, aquest enfocament ha demostrat no ser òptim: el filtrat podria eliminar informació que, al seu torn, podria no ser recuperable en el seguënt pas de realç. En la present Tesi doctoral es proposa un model basat en teoria de grafs per al processament d'imatges en color. En aquest model, es construïx un graf per a cada píxel de tal manera que les seues propietats permeten caracteritzar i classificar el píxel en quëstió. Com veurem, el model proposat és robust i capaç d'adaptar-se a una gran varietat d'aplicacions. En particular, apliquem el model per a crear noves solucions als dos problemes fonamentals del processament d'imatges: suavitzat i realç. S'ha estudiat el model en profunditat en funció del llindar, paràmetre clau que assegura la correcta classificació dels píxels de la imatge. A més, també s'han estudiat les possibles característiques i possibilitats del model que ens han permés traure-li el màxim partit en cadascuna de les possibles aplicacions. Basat en aquest model s'ha dissenyat un filtre adaptatiu capaç d'eliminar soroll gaussià d'una imatge sense difuminar els bords ni perdre informació dels detalls. A més, també ha permés desenvolupar un mètode capaç de realçar els bords i detalls d'una imatge al mateix temps que se suavitza el soroll present en la mateixa. Aquesta aplicació simultània aconseguix combinar dues operacions oposades per definició i superar així els inconvenients presentats per l'enfocament en dues etapes.[EN] Computer vision is one of the fastest growing fields at present which, along with other technologies such as Biometrics or Big Data, has become the focus of interest of many research projects and it is considered one of the technologies of the future. This broad field includes a plethora of digital image processing and analysis tasks. To guarantee the success of image analysis and other high-level processing tasks as 3D imaging or pattern recognition, it is critical to improve the quality of the raw images acquired. Nowadays all images are affected by different factors that hinder the achievement of optimal image quality, making digital image processing a fundamental step prior to the application of any other practical application. The most common of these factors are noise and poor acquisition conditions: noise artefacts hamper proper image interpretation of the image; and acquisition in poor lighting or exposure conditions, such as dynamic scenes, causes loss of image information that can be key for certain processing tasks. Image (pre-) processing steps known as smoothing and sharpening are commonly applied to overcome these inconveniences: Smoothing is aimed at reducing noise and sharpening at improving or recovering imprecise or damaged information of image details and edges with insufficient sharpness or blurred content that prevents optimal image (post-)processing. There are many methods for smoothing the noise in an image, however in many cases the filtering process causes blurring at the edges and details of the image. Besides, there are also many sharpening techniques, which try to combat the loss of information due to blurring of image texture and need to contemplate the existence of noise in the image they process. When dealing with a noisy image, any sharpening technique may amplify the noise. Although the intuitive idea to solve this last case would be the previous filtering and later sharpening, this approach has proved not to be optimal: the filtering could remove information that, in turn, may not be recoverable in the later sharpening step. In the present PhD dissertation we propose a model based on graph theory for color image processing from a vector approach. In this model, a graph is built for each pixel in such a way that its features allow to characterize and classify the pixel. As we will show, the model we proposed is robust and versatile: potentially able to adapt to a variety of applications. In particular, we apply the model to create new solutions for the two fundamentals problems in image processing: smoothing and sharpening. To approach high performance image smoothing we use the proposed model to determine if a pixel belongs to a at region or not, taking into account the need to achieve a high-precision classification even in the presence of noise. Thus, we build an adaptive soft-switching filter by employing the pixel classification to combine the outputs from a filter with high smoothing capability and a softer one to smooth edge/detail regions. Further, another application of our model allows to use pixels characterization to successfully perform a simultaneous smoothing and sharpening of color images. In this way, we address one of the classical challenges within the image processing field. We compare all the image processing techniques proposed with other state-of-the-art methods to show that they are competitive both from an objective (numerical) and visual evaluation point of view.Pérez Benito, C. (2019). Color Image Processing based on Graph Theory [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/123955TESI

    A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences

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    Abstract. The NLMeans filter, originally proposed by Buades et al., is a very popular filter for the removal of white Gaussian noise, due to its simplicity and excellent performance. The strength of this filter lies in exploiting the repetitive character of structures in images. However, to fully take advantage of the repetitivity a computationally extensive search for similar candidate blocks is indispensable. In previous work, we presented a number of algorithmic acceleration techniques for the NLMeans filter for still grayscale images. In this paper, we go one step further and incorporate both temporal information and color information into the NLMeans algorithm, in order to restore video sequences. Starting from our algorithmic acceleration techniques, we investigate how the NLMeans algorithm can be easily mapped onto recent parallel computing architectures. In particular, we consider the graphical processing unit (GPU), which is available on most recent computers. Our developments lead to a high-quality denoising filter that can process DVD-resolution video sequences in real-time on a mid-range GPU
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