Valérie De Witte

Fuzzy random impulse noise reduction method

A new two-step fuzzy filter that adopts a fuzzy logic approach for the enhancement of images corrupted with impulse noise is presented in this paper. The filtering method (entitled as Fuzzy Random Impulse Noise Reduction method (FRINR)) consists of a fuzzy detection mechanism and a fuzzy filtering method to remove (random-valued) impulse noise from corrupted images. Based on the criteria of peak-signal-to-noise-ratio (PSNR) and subjective evaluations we have found experimentally, that the proposed method provides a significant improvement on other state-of-the-art methods.

Histogram-based fuzzy colour filter for image restoration

A new impulse noise reduction method for colour images, called histogram-based fuzzy colour filter (HFC), is presented in this paper. The HFC filter is particularly effective for reducing high-impulse noise in digital images while preserving edge sharpness. Colour images that are corrupted with noise are generally filtered by applying a greyscale algorithm on each colour component separately. This approach causes artefacts especially on edge or texture pixels. Vector-based filtering methods were successfully introduced to overcome this problem. In this paper, we discuss an alternative technique so that no artefacts are introduced. The main difference between the new proposed method and the classical vector-based methods is the usage of colour component differences for the detection of impulse noise and the preservation of the colour component differences. The construction of the HFC filter involves three steps: (1) the estimation of the original histogram of the colour component differences, (2) the construction of suitable fuzzy sets for representing the linguistic values of these differences and (3) the construction of fuzzy rules that determine the output. Extensive simulation results show that the proposed filter outperforms many well-known filters (including vector-based approaches).

Video denoising by fuzzy motion and detail adaptive averaging

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.

Vector morphological operators for colour images

In this paper we extend the basic morphological operators dilation and erosion for grey-scale images based on the threshold approach, umbra approach and fuzzy set theory to colour images. This is realised by treating colours as vectors and defining a new vector ordering so that new colour morphological operators are presented. Here we only discuss colours represented in the RGB colour space. The colour space RGB becomes together with the new ordering and associated minimum and maximum operators a complete chain. All this can be extended to the colour spaces HSV and L*a*b*. Experimental results show that our method provides an improvement on the component-based approach of morphological operators applied to colour images. The colours in the colour images are preserved, that is, no new colours are introduced.

Fuzzy Impulse Noise Reduction Methods for Color Images

The reduction or removal of noise in a color image is an essential part of image processing, whether the final information is used for human perception or for an automatic inspection and analysis. In addition to all the classical based filters for noise reduction, many fuzzy inspired filters have been developed during the past years [3–26]. However, it is very difficult to judge the quality of all these different filters. For which noise types are they designed? How do they perform compared to each other? Are there some filters that clearly outperform the others? Do the numerical results correspond with the visual results? In this paper we answer these questions for color images that are corrupted with impulse noise. We also have developed a Java Applet (http://www.fuzzy.ugent.be/Dortmund.html). The Java Applet is used to compare all the mentioned filters with each other. It illustrates the numerical and visual performance of all these filters. Users have the possibility to load and corrupt an image from a predefined list.

A new fuzzy additive noise reduction method

In this paper we present a new alternative noise reduction method for color images that were corrupted with additive Gaussian noise. We illustrate that color images have to be processed in a different way than most of the state-of-the-art methods. The proposed method consists of two sub-filters. The main concern of the first subfilter is to distinguish between local variations due to noise and local variations due to image structures such as edges. This is realized by using the color component distances instead of component differences as done by most current filters. The second subfilter is used as a complementary filter which especially preserves differences between the color components. This is realized by calculating the local differences in the red, green and blue environment separately. These differences are then combined to calculate the local estimation of the central pixel. Experimental results show the feasibility of the proposed approach.

Morphological image interpolation to magnify images with sharp edges

In this paper we present an image interpolation method, based on mathematical morphology, to magnify images with sharp edges. Whereas a simple blow up of the image will introduce jagged edges, called ‘jaggies’, our method avoids these jaggies, by first detecting jagged edges in the trivial nearest neighbour interpolated image, making use of the hit-or-miss transformation, so that the edges become smoother. Experiments have shown that our method performs very well for the interpolation of ‘sharp’ images, like logos, cartoons and maps, for binary images and colour images with a restricted number of colours.

Fuzzy Similarity Measures for Colour Images

In this paper we give an overview of the possible application of fuzzy similarity measures to colour images. First of all, we review the component-based approach for the extension of similarity measures for greyscale image to colour images. And secondly, we discuss a vector-based approach using vector morphological operators. Both approaches are compared using several experiments

Gaussian noise reduction in greyscale images

The reduction of noise in an image, considered as a goal itself or as a pre-processing step, is an important issue. Besides classical filters, a wide variety of fuzzy filters has been developed. These filters use techniques from fuzzy set theory, and have the ability to incorporate the uncertainty that is involved in noise detection. However, it is very difficult to judge the quality of these filters. The goal of our comparative study is to select those filters that have the best performance for Gaussian noise reduction, and to investigate whether the use of fuzzy techniques represents a substantial improvement.

A novel histogram based fuzzy impulse noise restoration method for colour images

In this paper, we present a new restoration technique for colour images. This technique is developed for restoring colour images that are corrupted with impulse noise. The estimated histograms for the colour component differences (red-green, red-blue and green-blue) are used to construct fuzzy sets. Those fuzzy sets are then incorporated in a fuzzy rule based system in order to filter out the impulse noise. Experiments finally show the shortcomings of the conventional methods in contrast to the proposed method.