Fuzzy metrics and fuzzy logic for colour image filtering

El filtrado de imagen es una tarea fundamental para la mayoría de los sistemas de visión por computador cuando las imágenes se usan para análisis automático o, incluso, para inspección humana. De hecho, la presencia de ruido en una imagen puede ser un grave impedimento para las sucesivas tareas de procesamiento de imagen como, por ejemplo, la detección de bordes o el reconocimiento de patrones u objetos y, por lo tanto, el ruido debe ser reducido. En los últimos años el interés por utilizar imágenes en color se ha visto incrementado de forma significativa en una gran variedad de aplicaciones. Es por esto que el filtrado de imagen en color se ha convertido en un área de investigación interesante. Se ha observado ampliamente que las imágenes en color deben ser procesadas teniendo en cuenta la correlación existente entre los distintos canales de color de la imagen. En este sentido, la solución probablemente más conocida y estudiada es el enfoque vectorial. Las primeras soluciones de filtrado vectorial, como por ejemplo el filtro de mediana vectorial (VMF) o el filtro direccional vectorial (VDF), se basan en la teoría de la estadística robusta y, en consecuencia, son capaces de realizar un filtrado robusto. Desafortunadamente, estas técnicas no se adaptan a las características locales de la imagen, lo que implica que usualmente los bordes y detalles de las imágenes se emborronan y pierden calidad. A fin de solventar este problema, varios filtros vectoriales adaptativos se han propuesto recientemente. En la presente Tesis doctoral se han llevado a cabo dos tareas principales: (i) el estudio de la aplicabilidad de métricas difusas en tareas de procesamiento de imagen y (ii) el diseño de nuevos filtros para imagen en color que sacan provecho de las propiedades de las métricas difusas y la lógica difusa. Los resultados experimentales presentados en esta Tesis muestran que las métricas difusas y la lógica difusa son herramientas útiles para diseñar técnicas de filtrado,Morillas Gómez, S. (2007). Fuzzy metrics and fuzzy logic for colour image filtering [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1879Palanci

Análisis del Filtro FPGA en Imágenes de Tomografía Computarizada para la Reducción de Dosis Radiactiva

[EN] X-Ray or CT (computed tomography) images may have noise due to image acquisition process. As contaminated images complicate diagnosis many filters have been developed to overcome this problem. In this work we study the behavior of a Fuzzy method called FPGA, which detect and correct impulsive and Gaussian noise, used over a medical image obtained from the mini-MIAS database that has been altered with impulsive and/or Gaussian noise. The aim of the study is verify if FPGA is a candidate to be used as a method to reduce the radiation dose in CT. Results show that FPGA outperforms the rest of the methods studied and it reveals itself as a good candidate to be employed in CT images to reduce the radiation dose.[ES] Las imágenes de Rayos-X o de tomografía computarizada (CT) pueden contener ruido debido al proceso de adquisición. Este ruido complica sustancialmente el proceso diagnóstico, por lo que será necesario el desarrollo de filtros efectivos. En este trabajo se estudia el comportamiento del filtro Fuzzy Peer Group Averaging (FPGA) sobre una colección de imágenes mamográficas que ha sido previamente contaminada con ruido impulsivo y gaussiano. El objetivo del trabajo es averiguar si FPGA es adecuado para la mejora de imágenes CT obtenidas con una dosis de radiación reducida. Los resultados indican que FPGA se comporta, efectivamente, mejor que el resto de métodos estudiados en este trabajo y por tanto resulta un candidato adecuado.This work was partially funded by ANITRAN PROMETEO/2010/039, the Spanish Ministry of Science and Innovation (Project TIN2008-06570-C04-04), and the spin-off Titania (Grupo Dominguis).Parcero Iglesias, E.; Vidal Gimeno, VE.; Verdú Martín, GJ.; Josep Arnal García; Mayo Nogueira, P. (2014). Análisis del Filtro FPGA en Imágenes de Tomografía Computarizada para la Reducción de Dosis Radiactiva. Sociedad Nuclear Española. http://hdl.handle.net/10251/70824

Analysis of FPGA filter in computed tomography images for radioactive dose reduction

[EN] X-Ray or CT (computed tomography) images may have noise due to image acquisition process. As contaminated images complicate diagnosis many filters have been developed to overcome this problem. In this work we study the behavior of a Fuzzy method called FPGA, which detect and correct impulsive and Gaussian noise, used over a medical image obtained from the mini-MIAS database that has been altered with impulsive and/or Gaussian noise. The aim of the study is verify if FPGA is a candidate to be used as a method to reduce the radiation dose in CT. Results show that FPGA outperforms the rest of the methods studied and it reveals itself as a good candidate to be employed in CT images to reduce the radiation dose.[ES] Las imágenes de Rayos-X o de tomografía computarizada (CT) pueden contener ruido debido al proceso de adquisición. Este ruido complica sustancialmente el proceso diagnóstico, por lo que será necesario el desarrollo de filtros efectivos. En este trabajo se estudia el comportamiento del filtro Fuzzy Peer Group Averaging (Fuzzy PGA) sobre una colección de imágenes mamográficas que ha sido previamente contaminada con ruido impulsivo y gaussiano. El objetivo del trabajo es averiguar si Fuzzy PGA es adecuado para la mejora de imágenes CT obtenidas con una dosis de radiación reducida. Los resultados indican que Fuzzy PGA se comporta, efectivamente, mejor que el resto de métodos estudiados en este trabajo y por tanto resulta un candidato adecuado.Parcero Iglesias, E.; Vidal Gimeno, VE.; Verdú Martín, GJ.; Arnal García, J. (2014). Analysis of FPGA filter in computed tomography images for radioactive dose reduction. Grupo Senda. http://hdl.handle.net/10251/49701

Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation L1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i) each channel in a color image is contaminated independently, and contaminative components are independent and identically distributed; (ii) in a natural image the gradients of different components of a pixel are similar to one another. This method divides components into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the components are divided into the corrupted and the noise-free components; if the image is corrupted by random-valued impulses, the components are divided into the corrupted, noise-free, and the possibly corrupted components. Components falling into different categories are processed differently. If a component is corrupted, modified total variation diffusion is applied; if it is possibly corrupted, scaled total variation diffusion is applied; otherwise, the component is left unchanged. Simulation results demonstrate its effectiveness

Fuzzy averaging filter for impulse noise reduction in colour images with a correction step

[EN] In this paper we propose a fuzzy detection and reduction method for impulse noise in colour images. Detection is based on the fuzzyfication of a well-known statistic called ROD. The noise degrees obtained are used to reduce impulses by employing a fuzzy averaging between the input colour vector and a robust estimate of noise-free colour vector within the input neighbourhood. Fuzzy averaging has some advantages in terms of both noise reduction and detail preservation in front of detect and replace approaches because of threshold based decisions of the latter. However, robustness of the former is lower. We solve this problem by including a correction mechanism that checks the fuzzy noise degree of the output and replaces it with a robust colour vector either when noise has not been properly reduced or when a colour artefact has been introduced. We carry out a thorough study of the method parameter setting and give a convenient and robust setting. Experimental results show that our approach is very robust in front of four different types of impulse noise.The authors are very grateful to the reviewers for their valuable suggestions. Valentin Gregori and Samuel Morillas acknowledges the support of Ministry of Economy and Competitiveness of Spain under grant MTM 2015-64373-P (MINECO/FEDER, UE). Bernardino Roig and Almanzor Sapena acknowledges the support of Generalitat Valencians under grant AICO/2017/059.Gregori Gregori, V.; Morillas, S.; Roig, B.; Sapena Piera, A. (2018). Fuzzy averaging filter for impulse noise reduction in colour images with a correction step. Journal of Visual Communication and Image Representation. 55:518-528. https://doi.org/10.1016/j.jvcir.2018.06.025S5185285

A simple fuzzy method to remove mixed Gaussian-Impulsive noise from color images

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Mixed impulsive and Gaussian noise reduction from digital color images is a challenging task because it is necessary to appropriately process both types of noise that in turn need to be distinguished from the original image structures such as edges and details. Fuzzy theory is useful to build simple, efficient, and effective solutions for this problem. In this paper, we propose a fuzzy method to reduce Gaussian and impulsive noise from color images. Our method uses one only filtering operation: a weighted averaging. A fuzzy rule system is used to assign the weights in the averaging so that both noise types are reduced and image structures are reserved. We provide experimental results to show that the performance of the method is competitive with respect to state-of-the-art filters.This work was supported by the Spanish Ministry of Science and Innovation under Grant MTM2009-12872-C02-01.Camarena Estruch, JG.; Gregori Gregori, V.; Morillas, S.; Sapena Piera, A. (2013). A simple fuzzy method to remove mixed Gaussian-Impulsive noise from color images. IEEE Transactions on Fuzzy Systems. 21(5):971-978. https://doi.org/10.1109/TFUZZ.2012.2234754S97197821