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This chapter compares different optimization algorithms for contrast enhancement in grayscale images. The algorithms DE, HS, PSO, TLBO, ABC, FA and IWO which are classics in the literature for conducting this analysis, were addressed. All these methods were adapted with the same enhancement technique to observe the results they can offer. For the tests, a dataset commonly found in the literature was used, and 8 metrics were taken to measure the performance of the contrasted images: RMS, FSIM, ENTROPY, PSNR, MSE, VIF, IFC and Intensity levels. A deep analysis is carried out regarding metrics, fitness, and convergence, aiming to observe and contrast the results that each algorithm offers. Finally, conclusions and recommendations regarding the best method to improve the contrast in the images are presented.

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Optimization and Improve Image Contrast: A Comparative Study of Classical Metaheuristic Algorithms

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  • 发表日期：

    2025年

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  • Beatriz A. Rivera-Aguilar,
  • Diego Campos Peña,
  • Noé Ortega-Sánchez,
  • Alma Nayeli Rodríguez Vázquez

摘要

This chapter compares different optimization algorithms for contrast enhancement in grayscale images. The algorithms DE, HS, PSO, TLBO, ABC, FA and IWO which are classics in the literature for conducting this analysis, were addressed. All these methods were adapted with the same enhancement technique to observe the results they can offer. For the tests, a dataset commonly found in the literature was used, and 8 metrics were taken to measure the performance of the contrasted images: RMS, FSIM, ENTROPY, PSNR, MSE, VIF, IFC and Intensity levels. A deep analysis is carried out regarding metrics, fitness, and convergence, aiming to observe and contrast the results that each algorithm offers. Finally, conclusions and recommendations regarding the best method to improve the contrast in the images are presented.