λ-enhancement: contrast adaptation based on optimization of image fuzziness

Tizhoosh, Hamid R.; Krell, Gerald; Michaelis, Bernd

doi:10.1109/fuzzy.1998.686349

Cited by 19 publications

(25 citation statements)

References 4 publications

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“…Among quite a few works of enhancement evaluation reported in literature, the one in [16] employs a fuzziness-based image quality measure. We shall now consider this image quality measure for enhancement evaluation and compare it to the use of the proposed logarithmic tolerance fuzzy rough-fuzzy entropy.…”

Section: A Enhancement Evaluationmentioning

confidence: 99%

“…If it is considered that the quality of an image is a term that describes how well its different parts are distinguishable, then the proposed AIA measures in (36) can readily be used as image quality measures for quantitative evaluation of image enhancement with a smaller value signifying better quality. Note that, on the contrary, a larger value of the measure used in [16] means better image quality.…”

Section: A Enhancement Evaluationmentioning

confidence: 99%

“…5(b) and (c) are the enhanced ones using the histogram equalization [17] and unsharp masking [17] techniques, respectively. The image quality measure used in [16] Fig. 5(c) has the best quality; as in Fig.…”

Section: A Enhancement Evaluationmentioning

confidence: 99%

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Generalized Rough Sets, Entropy, and Image Ambiguity Measures

Sen

Pal

2009

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Quantifying ambiguities in images using fuzzy set theory has been of utmost interest to researchers in the field of image processing. In this paper, we present the use of rough set theory and its certain generalizations for quantifying ambiguities in images and compare it to the use of fuzzy set theory. We propose classes of entropy measures based on rough set theory and its certain generalizations, and perform rigorous theoretical analysis to provide some properties which they satisfy. Grayness and spatial ambiguities in images are then quantified using the proposed entropy measures. We demonstrate the utility and effectiveness of the proposed entropy measures by considering some elementary image processing applications. We also propose a new measure called average image ambiguity in this context.

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Section: A Enhancement Evaluationmentioning

confidence: 99%

Section: A Enhancement Evaluationmentioning

confidence: 99%

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Generalized Rough Sets, Entropy, and Image Ambiguity Measures

Sen

Pal

2009

IEEE Trans. Syst., Man, Cybern. B

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“…Based on Sugeno's definition, we intuitively define the following class of involutive membership functions [45]:…”

Section: A-enhancementmentioning

confidence: 99%

Fuzzy Image Enhancement: An Overview

Tizhoosh¹

2000

Fuzzy Techniques in Image Processing

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“…Image enhancement is also done by measuring information contained in the image [11,13,21,22]. The membership function is chosen based on the measured quantity such as image entropy or index of fuzziness.…”

Section: Introductionmentioning

confidence: 99%