THE USE OF MARKOV CHAINS IN IMAGE FILTERING

Abstract

Images play an important role in the organization of our society. Most media (e.g. newspapers, TV, cinema) use pictures (still or moving) as information caniers. The tremendous volume of optical information and the need for its processing and transmission paved the way to image processing by digital computers. The relevant efforts started around 1964 at the Jet Propulsion Laboratory (Pasadena, California) and concerned the digital processing of satellite images coming from the moon. Soon, a new branch of science called digital image processing emerged. Since then, it has exhibited a tremendous growth and created an important technological impact in several areas, e.g. in telecommunications, TV broadcasting, industry, medical imaging, GIS, remote sensing and scientific research. But images are often deteriorated by noise due to various sources of interference and other phenomena that affect the measurement processes in imaging and data acquisition systems. Image filtering techniques are mathematical techniques that are aimed at realizing improvement in the quality of a given image. The result is another image that demonstrates certain features in a manner that is better in some sense as compared to their appearance in the original image. One may also derive or compute multiple processed versions of the original image, each presenting a selected feature in an enhanced appearance. Simple image filtering techniques are developed and applied in an ad hoc manner. Advanced techniques that are optimized with reference to certain specific requirement and objective criteria are also available. Although most filtering techniques are applied with the aim of generating improved images for use by a human observer, some techniques are used to derive images that are meant for use by a subsequent algorithm for computer processing. If used inappropriately, filtering techniques themselves may increase

noise while improving contrast, they may eliminate small details and edge sharpness while improving noise, and they may produce artifacts in general.