Fuzzy logic applied to low contrast digital images
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Fuzzy logic applied to low contrast digital images

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Published by UMIST in Manchester .
Written in English


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Edition Notes

StatementS.J. Thomson ; supervised by P.A. Gaydecki.
ContributionsGaydecki, P. A., DIAS.
ID Numbers
Open LibraryOL21239515M

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Fuzzy logic based contrast enhancement techniques have been proven improving the contrast in digital images. As a result, such image creates side-effects such as washed-out appearance and Fuzzy mathematical morphology is an extension of binary morphology to gray-scale images using techniques from fuzzy logic. Fuzzy mathematical morphology can be applied The new integrated approach has the capability to enhance the contrast in digital images in efficient manner by using the modified fuzzy based enhancement algorithm. low contrast and image   We have applied the proposed algorithm to a variety of images. As mentioned before, the commonly used techniques for contrast enhancement can be categorized as indirect methods of contrast enhancement and direct methods. Histogram specification and histogram equalization are two most popular indirect contrast enhancement ant Dash and Chatterji, Dhnawan et al., and

  We generated the speckle noise using the model described in. Fig. 1 is a low contrast image. Fig. 1(a) is the original image which has two ellipses with intensities 30 respectively; one circle with intensity 30; and two rectangles with intensities 30 respectively; and the uniform background has intensity From Fig. 1 we can observe: (1) The proposed method can almost reduce   The fuzziï¬ er and intensiï¬ cation parameters are evaluated automatically for the input color image, by optimizing the contrast and entropy in the fuzzy domain. The method has been applied to various test images and found suitable for enhancement of low contrast and low intensity color ://   Miosso, C.J., Bauchspiess, A.: Fuzzy Inference System Applied to Edge Detection in Digital Images. In: Proceedings of the V Brazilian Conference on   The difficulty of this goal has caused the field to focus on smaller, more constrained problems related with the different tasks involved, such as: noise removal, smoothing, and sharpening of contrast -low-level-; segmentation of images to isolate objects and regions, and description and recognition of the segmented regions -intermediate-level

  The field of digital image processing refers to processing digital images by means of a digital computer (查看原文) 百万de心Logos —— 引自第31页 Fuzzy logic-based histogram equalization (FHE) is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and Fuzzy logic has been applied 9 In this paper we propose an efficient method to enhance contrast of digital images. Image contrast enhancement is a pre-precessing step that improves efficiency