Memetic Comp. (2010) 2:283–304 DOI 10.1007/s12293-010-0046-3 REGULAR RESEARCH PAPER New advances in digital image processing Annamária R. Várkonyi-Kóczy Received: 22 July 2009 / Accepted: 2 July 2010 / Published online: 25 July 2010 © Springer-Verlag 2010 Abstract Enhancement of noisy image data is a very chal- lenging issue in many research and application areas. In the last few years, non-linear filters, feature extraction, high dynamic range 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. In this paper new image processing tech- niques are introduced in the above mentioned fields, 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 perfor- mance, and parallel with it (3) to decrease the complexity of further processing using the results of the image processing phase. Keywords Digital image processing · Soft computing techniques · Fuzzy logic · Information enhancement · Feature extraction 1 Introduction With the continued growth of multimedia and communica- tion systems, the instrumentation and measurement fields have seen a steady increase in the focus on image data. Images contain measurement information of key interest for a variety of research and application areas such as astronomy, remote sensing, biology, medical sciences, particle physics, science of materials, etc. Developing tools and techniques to enhance A. R. Várkonyi-Kóczy (B ) Institute of Mechatronics and Vehicle Engineering, Óbuda University, Népszínház u. 8, Budapest 1081, Hungary e-mail: varkonyi-koczy@uni-obuda.hu the quality of image data plays, in any case, a very rele- vant role. Enhancement of noisy images, however, is not a trivial task. The filtering action should distinguish between unwanted noise (to be removed) and image details (to be pre- served or possibly enhance). Soft computing, and especially evolutionary and fuzzy systems based methods can effec- tively complete this task outperforming conventional meth- ods [1]. Indeed, genetic algorithms and evolutionary methods proved to be very advantageous in image analysis, search, and optimization while fuzzy reasoning is very well suited to model uncertainty that typically occurs when both noise cancellation and detail preservation (enhancement) repre- sent very critical issues. As a result, the number of different approaches to evolutionary and fuzzy image processing has been progressively increasing (see e.g. [2–4]). In this paper we deal with different areas of image pro- cessing and introduce new soft computing (fuzzy) supported methods. In Sect. 2 corner detection is addressed. Section 3 deals with useful information extraction. “Useful” informa- tion means that the information is important from the further processing point of view and the, from this aspect non-impor- tant (in other situations possibly significant) image informa- tion is handled as noise, i.e. is filtered out. In this section, we present a method for separating the primary and non-primary edges in the images. Sections 4 and 5 are devoted to high dynamic range (HDR) imaging. Novel approaches are detailed for reproduction of images distorted by the HDR of illumination. Finally, Sect. 6 shows illustrative examples. 2 Corner detection Recently, the significance of feature extraction, e.g. corner detection has increased in computer vision, as well in related 123