Abstract
Enhancement of noisy image data is a very challenging issue in many research and application areas. In the last few years, non-linear filters, feature extraction, high dynamic range (HDR) imaging methods based on soft computing models have been shown to be very effective in removing noise without destroying the useful information contained in the image data. Although, to distinguish among noise and useful information is not an easy task and may highly depend on the situation and aim of the processing. In this chapter new image processing techniques are introduced in the field of image quality improvement, thus contributing to the variety of advantageous possibilities to be applied. The main intentions of the presented algorithms are (1) to improve the quality of the image from the point of view of the aim of the processing, (2) to support the performance, and parallel with it (3) to decrease the complexity of further processing using the results of the image processing phase.
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Várkonyi-Kóczy, A.R. (2011). Low Complexity Situational Models in Image Quality Improvement. In: Ruano, A.E., Várkonyi-Kóczy, A.R. (eds) New Advances in Intelligent Signal Processing. Studies in Computational Intelligence, vol 372. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11739-8_8
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DOI: https://doi.org/10.1007/978-3-642-11739-8_8
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