Distance Measures for Reduced Ordering Based Vector Filters

Reduced ordering based vector filters have proved successful in removing long-tailed noise from color images while preserving edges and fine image details. These filters commonly utilize variants of the Minkowski distance to order the color vectors with the aim of distinguishing between noisy and noise-free vectors. In this paper, we review various alternative distance measures and evaluate their performance on a large and diverse set of images using several effectiveness and efficiency criteria. The results demonstrate that there are in fact strong alternatives to the popular Minkowski metrics

Implementation of Impulse Noise Reduction Method to Color Images using Fuzzy Logic

Image Processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. Impulse noise reduction method is one of the critical techniques to reduce the noise in color images. In this paper the impulse noise reduction method for color images by using Fuzzy Logic is implemented. Generally Grayscale algorithm is used to filter the impulse noise in corrupted color images by separate the each color component or using a vector-based approach where each pixel is considered as a single vector. In this paper the concepts of Fuzzy logic has been used in order to distinguish between noise and image characters and filter only the corrupted pixels while preserving the color and the edge sharpness. Due to this a good noise reduction performance is achieved. The main difference between this method and other classical noise reduction methods is that the color information is taken into account to develop a better impulse noise detection a noise reduction that filters only the corrupted pixels while preserving the color and the edge sharpness. The Fuzzy based impulse noise reduction method is implemented on set of selected images and the obtained results are presented

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

Fast Method Based on Fuzzy Logic for Gaussian-Impulsive Noise Reduction in CT Medical Images

To remove Gaussian-impulsive mixed noise in CT medical images, a parallel filter based on fuzzy logic is applied. The used methodology is structured in two steps. A method based on a fuzzy metric is applied to remove the impulsive noise at the first step. To reduce Gaussian noise, at the second step, a fuzzy peer group filter is used on the filtered image obtained at the first step. A comparative analysis with state-of-the-art methods is performed on CT medical images using qualitative and quantitative measures evidencing the effectiveness of the proposed algorithm. The parallel method is parallelized on shared memory multiprocessors. After applying parallel computing strategies, the obtained computing times indicate that the introduced filter enables to reduce Gaussian-impulse mixed noise on CT medical images in real-time.This research was funded by the Spanish Ministry of Science, Innovation and Universities (Grant RTI2018-098156-B-C54), and it was co-financed with FEDER funds