Enhancement of low exposure images via recursive histogram equalization algorithms

Singh, Kulbir; Kapoor, Rajiv; Sinha, Sanjeev

doi:10.1016/j.ijleo.2015.06.060

Cited by 146 publications

(68 citation statements)

References 21 publications

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“…A sub-histogram based approach was developed and reported in [13] where the adverse effects of under and over exposure were reduced. A similar approach was also reported in [14] where a recursive loop was included. In [15], the authors used the selection of multiple histogram references to achieve their brightness objective.…”

Section: Introductionmentioning

confidence: 94%

Histogram equalization and optimal profile compression based approach for colour image enhancement

Wong

Jiang

Rahman

et al. 2016

Journal of Visual Communication and Image Representation

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Section: Introductionmentioning

confidence: 94%

Histogram equalization and optimal profile compression based approach for colour image enhancement

Wong

Jiang

Rahman

et al. 2016

Journal of Visual Communication and Image Representation

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“…The ESIHE method clips the input histogram at the average number of intensity occurrences and segments the clipped histogram using a threshold based on the image exposure. Singh et al present a recursive-division-based extension of ESIHE, referred as RS-ESIHE [19]. RS-ESIHE performs recursive divisions of the histogram based on the image exposure.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed approach is compared with conventional as well as state-of-the-art contrast-enhancement algorithms. The comparison algorithms are BBHE [8], DSIHE [9], RMSHE [14], MMBEBHE [10], RSIHE [15], DHE [11], BHEPL [16], ESIHE [19], MMSICHE [21], and BHEMHB [27]. The reason of choosing these methods for comparison is that they are mainly based on histogram segmentation, histogram clipping, and/or histogram modification.…”

Section: Data Samples and Performance Evaluationsmentioning

confidence: 99%

Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

Zhuang

Guan

2018

Computational Intelligence and Neuroscience

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A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.

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“…From the literature survey, the studies of Kuldeep Singh, et al [6] shows that in their technique thresholding value is found out then all pixels are greater than threshold, all set to threshold value. Because of this operation no.…”

Section: Motivationmentioning

confidence: 99%

Underwater Low Contrast Color Images Enhancement based on Dynamic Thresholding and Stretching

Singh¹,

Jarial²

2017

IJCATR

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Abstract:In the modern era, the underwater images are captures to monitor the internal conditions of water. The underwater images capturing is difficult task because the water medium provides the different challenges like absorption, scattering, diffusion etc. To resolve these issues enhancement and color correction plays an important role. In this paper, the underwater images enhancement is done using exposure value, dynamic thresholding and stretching. The benefits of dynamic thresholding and stretching is that the naturalness of the image never loss and incontrollable manners the image stretched. Also, performance analysis of proposed technique is done with existing technique based on Qualitative and Quantitative analysis. The results reflect that the proposed technique have better enhancement and higher entropy as compared to existing technique.

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Enhancement of low exposure images via recursive histogram equalization algorithms

Cited by 146 publications

References 21 publications

Histogram equalization and optimal profile compression based approach for colour image enhancement

Histogram equalization and optimal profile compression based approach for colour image enhancement

Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

Underwater Low Contrast Color Images Enhancement based on Dynamic Thresholding and Stretching

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