Contrast Entropy Based Image Enhancement and Logarithmic Transform Coefficient Histogram Shifting

Silver, Blair; Agaian, Sos S.; Panetta, Karen

doi:10.1109/icassp.2005.1415484

Cited by 16 publications

(4 citation statements)

References 6 publications

(8 reference statements)

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“…Dataset, Algorithms and Quantitative Measures 1) Dataset and Algorithms: We use standard natural test images from several datasets. The Berkeley 500 image dataset [20] is used to evaluate and compare SECE and SECEDCT, both qualitatively and quantitatively, with our implementations of GHE, EHS [13], WTHE [14], LTHM [4]- [6], LTHS [4]- [6], the weighted histogram approximation of HMF [15], CVC [18], 2DHE [2], and AGCWD [16]. The tests of significance of the quantitative measures are performed on 500 natural images of Berkeley dataset [20].…”

Section: Resultsmentioning

confidence: 99%

“…A "graying out" can also occur resulting in the image of the scene tending to middle gray. In [4]- [6], three different transform domain (discrete cosine transform) contrast enhancement algorithms are proposed: a) logarithmic transform histogram matching (LTHM), b) logarithmic transform histogram shifting (LTHS), and c) logarithmic transform histogram shaping using Gaussian distributions (LTHSG). In general, transform domain coefficients are modified according to a mapping of transform domain coefficient distribution to a target distribution and then inverse transform (inverse discrete cosine transform) is applied to obtain contrast enhanced image.…”

Section: Related Workmentioning

confidence: 99%

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Spatial Entropy-Based Global and Local Image Contrast Enhancement

Çelik

2014

IEEE Trans. on Image Process.

181

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This paper proposes a novel algorithm, which enhances the contrast of an input image using spatial information of pixels. The algorithm introduces a new method to compute the spatial entropy of pixels using spatial distribution of pixel gray levels. Different than the conventional methods, this algorithm considers the distribution of spatial locations of gray levels of an image instead of gray-level distribution or joint statistics computed from the gray levels of an image. For each gray level, the corresponding spatial distribution is computed using a histogram of spatial locations of all pixels with the same gray level. Entropy measures are calculated from the spatial distributions of gray levels of an image to create a distribution function, which is further mapped to a uniform distribution function to achieve the final contrast enhancement. The method achieves contrast improvement in the case of low-contrast images; however, it does not alter the image if the image’s contrast is high enough. Thus, it always produces visually pleasing results without distortions. Furthermore, this method is combined with transform domain coefficient weighting to achieve both local and global contrast enhancement at the same time. The level of the local contrast enhancement can be controlled. Several experiments on effects of contrast enhancement are performed. Experimental results show that the proposed algorithms produce better or comparable enhanced images than several state-of-the-art algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Spatial Entropy-Based Global and Local Image Contrast Enhancement

Çelik

2014

IEEE Trans. on Image Process.

181

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“…The first of these algorithms is logarithmic transform histogram coefficient shifting, or LTCHS. It involves taking a histogram of the logarithmic transform coefficients and applying a shift in the positive direction [16]. The second of these is logarithmic transform histogram matching, or LTCHM.…”

Section: Logarithmic Domain Enhancement Algorithmsmentioning

confidence: 99%

Comparative study of logarithmic enhancement algorithms with performance measure

2006

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Performance measures of image enhancement are traditionally subjective and have difficulty quantifying the improvement made by the algorithm. In this paper, we present the image enhancement measures and show how utilizing logarithmic arithmetic based addition, subtraction, and multiplication provides better results than previously used measures. In addition, for illustration of the performance of developed measures, we present a comprehensive study of several image enhancement algorithms from all three domains, including spatial, transform, and logarithmic algorithms.

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“…For example, is there a way of somehow combining histogram equalization and transform enhancement? The answer: Yes, using transform histograms [9], [10], which have, as of yet, been mostly unexplored. This paper explores three new methods for which transform histograms can be utilized for contrast enhancement of images: logarithmic transform histogram mapping, logarithmic transform histogram shifting, and logarithmic transform histogram shaping.…”

Section: Introductionmentioning

confidence: 96%

Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

Agaian

Silver

Panetta

2007

IEEE Trans. on Image Process.

479

203

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Many applications of histograms for the purposes of image processing are well known. However, applying this process to the transform domain by way of a transform coefficient histogram has not yet been fully explored. This paper proposes three methods of image enhancement: a) logarithmic transform histogram matching, b) logarithmic transform histogram shifting, and c) logarithmic transform histogram shaping using Gaussian distributions. They are based on the properties of the logarithmic transform domain histogram and histogram equalization. The presented algorithms use the fact that the relationship between stimulus and perception is logarithmic and afford a marriage between enhancement qualities and computational efficiency. A human visual system-based quantitative measurement of image contrast improvement is also defined. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.

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Contrast Entropy Based Image Enhancement and Logarithmic Transform Coefficient Histogram Shifting

Cited by 16 publications

References 6 publications

Spatial Entropy-Based Global and Local Image Contrast Enhancement

Spatial Entropy-Based Global and Local Image Contrast Enhancement

Comparative study of logarithmic enhancement algorithms with performance measure

Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

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