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

In this paper, the notions of fuzzy T-metric and fuzzy S-metric have been introduced, and then, examples of known fuzzy metrics are provided, as well as theorems that enable algorithms for putting up new metrics. Recently, there has been renewed interest in some of their properties, which are further shown, these being polygonal inequality, and two new classes of functions have been shown to be regarded as fuzzy metric. By applying these fuzzy metrics, an algorithm has been used in order to remove image noise. The goal was to improve the sharpness and the quality of the image, which is expressed and measured by means of the image quality index UIQI. It has been shown that the general image, which is filtered by this algorithm, has greater sharpness than the image filtered by the median filter, which is probably the most commonly used vector filter.

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“…For additional literature reading about fuzzy filtering, authors recommend the following list: [5], [13], [14], [15] and [20].…”

Section: Applicationmentioning

confidence: 99%

Fuzzy metric space and applications in image processing

Ralević¹,

Paunović²,

Iričanin³

2020

MathMon

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“…For instance, in one-pixel attacks, altering only few pixels in an original image is enough to fool a deep neural network [6,7]. Therefore, filtering algorithms dedicated to the suppression of impulsive disturbances in color images and also considered as defensive methods against adversarial attacks, have attracted considerable interest among many researchers [8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Is large improvement in efficiency of impulsive noise removal in color images still possible?

2021

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The substantial improvement in the efficiency of switching filters, intended for the removal of impulsive noise within color images is described. Numerous noisy pixel detection and replacement techniques are evaluated, where the filtering performance for color images and subsequent results are assessed using statistical reasoning. Denoising efficiency for the applied detection and interpolation techniques are assessed when the location of corrupted pixels are identified by noisy pixel detection algorithms and also in the scenario when they are already known. The results show that improvement in objective quality measures can be achieved by using more robust detection techniques, combined with novel methods of corrupted pixel restoration. A significant increase in the image denoising performance is achieved for both pixel detection and interpolation, surpassing current filtering methods especially via the application of a convolutional network. The interpolation techniques used in the image inpainting methods also significantly increased the efficiency of impulsive noise removal.

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“…The median filter and its different variants are extensively used [22] to reduce the impulse noise from grayscale images and the performance is increased. Averaging the pixel intensities with respect to the size of the filter is a common method for smoothing the image, but fuzzy averaging [23] reduces impulses in a large way. Identify the pixels belonging to the borders, then apply a reduced smoothing and applying more intense smoothing to the remaining pixels produced a standard result [24] in the ultrasound image application.…”

Section: Introductionmentioning

confidence: 99%

Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage

Mishra¹,

Srinivas²

2021

IJ-AI

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<p><span lang="EN-US">The facial emotion recognition by the machine is a challenging task. From decades, researchers applied different methods to classify facial emotion into the different classes. The expansion of artificial intelligence in a form of deep convolutional neural network (CNN) changed the direction of the research. The facial emotion recognition using deep CNN is powerful in terms of taking bulk input images for processing and classify with high accuracy. It has been noticed in a few cases the classification model does not judge the facial images into appropriate classes due to the influence of noises. So, it is highly recommended to apply a noiseless image to the facial emotion recognition model for classification. We adopted a mechanism and proposed a model for classifying facial image into one of the seven classes with high accuracy. The images are smoothed before applying to the model by different smoothing process as part of image preprocessing. We claim facial emotion recognition with image smoothing by different filters or a mixture of filter are more robust than without preprocessing. The detail is explained in the subsequent sections.</span></p>

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

Cited by 19 publications

References 31 publications

Fuzzy metric space and applications in image processing

Fuzzy metric space and applications in image processing

Is large improvement in efficiency of impulsive noise removal in color images still possible?

Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage

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