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Swarm-based optimally selected histogram computation system for image enhancement

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Abstract

In this paper, two newly proposed evolutionary computational algorithms (ECAs) are joined with Otsu thresholding method to achieve brightness preserving image contrast enhancement. The selected ECAs are the whale optimization algorithm (WOA) and the crow search algorithm (CSA) which embrace more contrast and minimum entropy alteration corresponding to the original image. The proposed algorithm employs histogram equalization method, based on an improved cumulative distribution to calculate a mapping function. Therefore, a 3-stage procedure has been assumed to change the original histogram. The primary step of the proposed method is to sub-divide the image histogram by using the Otsu thresholding technique. Further, both of histograms are weighted and thresholded in order to control the level of enhancement. The constraint parameters are attained by WOA and CSA algorithms for modification. After constraining the histograms, mean shift modification is executed to slight altering the position of mean shifting from input to output image. The results reflect that proposed technique accomplishes balanced contrast enhancement and better color preservation in comparison with surviving techniques. Through the proposed technique, the enhanced images achieve low contrast boosting, a good trade-off between detail improvement, and brightness conservation with naturalness of the input image.

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Abbreviations

HE:

Histogram equalization

CDF:

Cumulative density function

QDHE:

Quadrants dynamic histogram equalization

DSIHE:

Dualistic sub image histogram equalization

BBHE:

Brightness preserving bi-histogram equalization

RMSHE:

Recursive mean-separate histogram equalization

BHEPL:

Bi-histogram equalization plateau limit

SHMS:

Simple histogram modification scheme

WAHE:

Weighted histogram approximation method

PSO:

Particle swarm optimization

ABC:

Artificial bee colony

GA:

Genetic algorithm

CS:

Cuckoo search algorithm

WDO:

Wind driven optimization

BFO:

Bacteria swarm optimization

DE:

Differential evolution

WOA:

Whale optimization algorithm

CSA:

Crow search algorithm

GWO:

Gray wolf optimization

CT:

Computed tomography

NSCT:

Non-subsampled contourlet transform

PDF:

Probability density function

PSNR:

Peak signal-to-noise ratio

SSIM:

Structural similarity index

DE:

Discrete entropy

CPP:

Contrast per pixel

GMSD:

Gradient magnitude similarity deviation

MEME:

Modified measure of enhancement

AMBE:

Absolute mean brightness error

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, 800005, India

    Ashish Kumar Bhandari & Neha Singh

  2. Department of Electronics and Communication Engineering, International Institute of Information Technology, Naya Raipur, India

    Anurag Singh

Authors
  1. Ashish Kumar Bhandari
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  2. Neha Singh
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  3. Anurag Singh
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Correspondence to Ashish Kumar Bhandari.

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Bhandari, A.K., Singh, N. & Singh, A. Swarm-based optimally selected histogram computation system for image enhancement. Neural Comput & Applic 34, 7053–7067 (2022). https://doi.org/10.1007/s00521-021-06858-y

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