By Amitava Chatterjee, Patrick Siarry
Computational intelligence established innovations have firmly proven themselves as possible, trade, mathematical instruments for greater than a decade. they've been greatly hired in lots of structures and alertness domain names, between those sign processing, computerized regulate, commercial and shopper electronics, robotics, finance, production structures, electrical energy platforms, and tool electronics. photo processing is additionally a very powerful zone which has attracted the attention of many researchers who're attracted to the advance of latest computational intelligence-based innovations and their appropriate functions, in either learn problems and in real-world difficulties.
Part I of the publication discusses a number of photograph preprocessing algorithms; half II greatly covers photograph compression algorithms; half III demonstrates how computational intelligence-based ideas will be successfully applied for photograph research reasons; and half IV indicates how development reputation, type and clustering-based innovations will be built for the aim of photograph inferencing. The ebook bargains a unified view of the fashionable computational intelligence techniques required to resolve real-world difficulties and it truly is compatible as a reference for engineers, researchers and graduate students.
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Extra resources for Computational Intelligence in Image Processing
M. Kwok et al. 14. : Superresolution reconstruction of multispectral data for improved classification. IEEE Geosci. Remote Sens. Lett. 6(4), 689–693 (2009) 15. : Hue-preserving color image enhancement without gamut problem. IEEE Trans. Image Process. 12(12), 1591–1598 (2003) 16. : Virtual restoration of ancient chinese paintings using color contrast enhancement and lacuna texture synthesis. IEEE Trans. Image Process. 13(3), 416–429 (2004) 17. : Image sharpening by morphological filtering. Pattern Recognit.
Int. J. Electron. Commun. (AEÜ) 59, 463–472 (2005) Chapter 2 Locally-Equalized Image Contrast Enhancement Using PSO-Tuned Sectorized Equalization N. M. Kwok, D. Wang, Q. P. Ha, G. Fang and S. Y. Chen Abstract Contrast enhancement is a fundamental procedure in applications requiring image processing. Indeed, image enhancement contributes critically to the success of subsequent operations such as feature detection, pattern recognition and other higher-level processing tasks. Of interest among methods available for contrast enhancement is the intensity modification approach, which is based on the statistics of pixels in a given image.
It is seen that the information content with these methods generally drops down in value because part of the intensity range has not been fully utilized. This is indicated by the zero entries in the histogram as shown in Figs. 7e. The third column is obtained from results using the CLAHE approach where improvements in the information are noticeable. From column 4 to column 13, the entropies are shown with respect to the iterations performed during the proposed contrast enhancement process. It is evident that the proposed method has made an overall improvement over other methods implemented in the test.