Gustavo Camps i Valls⁽¹⁾, Antonio J. Serrano López⁽²⁾, Jesús Modia⁽³⁾, José David Martín Guerrero⁽⁴⁾, José Vicente Francés Víllora^(5)
(1)Universitat de València - Burjassot, Valencia. Spain
⁽²⁾⁽³⁾Dpto. Electrónica. Universidad de Valencia - Burjasot. Spain
⁽⁴⁾G.P.D.S. Departament d´Enginyeria Electrònica. Universitat de València - Burjassot. Spain
⁽⁵⁾Dpto. Ingenieria Electrónica. University of Valencia - Burjassot. Spain

	[Health Informatics]	[Medical Electronics & Engineering]

INTRODUCTION

Any image acquired by a device is susceptible of being degraded by the environment of acquisition; phenomenons of noise of the sensor can appear, blurring because of an unfocused objective, movement, variations in the illumination, etc. The restoration of images tries to minimize the effects of these degradations by means of a filtrate. It is distinguished from enhancement techniques in that those are in charge of more for the accentuation and extraction of characteristics than of their restoration.

Therefore, a fundamental problem in the image processing is the improvement of their quality through the reduction of the noise that they can contain being often known as "cleaning of images". A great variety of techniques dedicated to carry out this task exists and each one of them is focused on different features from the image and the noise.

When the noise has some characteristic pattern not completely aleatory in time, and therefore it doesn´t present an analyzable behavior from the statistical point of view, it is much more difficult to establish numeric describers on the quantity of noise. However, they are used the same techniques approximately in the elimination of the noise producing a smaller effectiveness.

In the field of images restoration it is assumed, in most of methods, that pixels in the image are much smaller that any detail and that many of their neighbors represent the same structure. With these assumptions, it is possible to apply methods based on substituting any pixel with its averaged vicinity to restore or to clean images with random noise.

A great variety of techniques dedicated to carry out this task exists and each one of them is guided to different characteristics from the image and the noise. With some different suppositions and a completely particular way is approached each problem: this will make an useful technique in the elimination of certain characteristics of noise not to be it so much before others. The simplest procedure of averaging spacely is simply made by adding the values of brightness of each píxel in each region of the image and to divide by the number of pixels of the environment, using this result in the construction of a new image.

The vicinity operations that include the multiplication for kernels are usually applied using symmetry around the pixel. This creates a problem for the pixels near the borders of the image since they have half of neighboring pixels. To solve this sort of problems many techniques have been devised: starting from masks or asymmetric kernels to special marginal conditions.

Varying the values of the weights of these kernels then a set of masks more or less effective in the cleaning of the noise will be found but they will have as a common effect, and at the same time undesired, the blurring of the image; this is the case of the well-known median filter.

Discussion Board

Any Comment to this presentation?

[ABSTRACT] [INTRODUCTION] [SELECTIVE MEDIAN FILTER ALGORITHM] [RESULTS] [FIGURES] [DISCUSSION] [REFERENCES] [Discussion Board]

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	[Health Informatics]	[Medical Electronics & Engineering]

Gustavo Camps i Valls, Antonio J. Serrano López, Jesús Modia, José David Martín Guerrero, José Vicente Francés Víllora
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Last update: 17/01/00