Image Contrast Enhancement Using Artificial Hummingbird Algorithm

Abstract: Image enhancement plays an important role in medical imaging, satellite image processing, and computer vision. These fields require high-quality images to improve the visibility of details, which is essential for accurate analysis and interpretation. Contrast adjustment, histogram equalization, and gamma correction are the traditional image enhancement techniques that are widely used. However, these methods often fall short, particularly when applied uniformly across diverse images, as they may not always yield the best results. We provide a unique picture enhancing method based on the Artificial Hummingbird Algorithm (AHA) to overcome these drawbacks. The AHA is inspired by the foraging behaviour of hummingbirds, which are known for their efficient and adaptive search strategies. This bio-inspired algorithm leverages strategies to optimize the gamma correction parameter, aiming to enhance image contrast and overall visual quality. The aim is to optimize the gamma correction parameter to maximize fitness values derived from several image quality metrics, including Structural Similarity Index Measure (SSIM), Feature Similarity Index Measure (FSIM), and Peak Signal-to-Noise Ratio (PSNR). The AHA operates through a population-based approach, where each individual in the population represents a potential solution with a specific gamma value. Initially, a population of random gamma values is generated, and each value is used to enhance the image, followed by evaluating its quality using the combined fitness function. The algorithm uses three distinct foraging strategies to efficiently explore the solution space: guided, territorial and migration foraging. Guided foraging helps steer the search towards promising areas, while territorial foraging refines the local search. Migration foraging periodically resets some individuals to avoid premature convergence and ensure a comprehensive exploration of the solution space. Through iterative adaptation and evolution, the AHA continuously updates and refines the best gamma value found, leading to significantly enhanced images. Experimental results demonstrate that the AHA-based technique offers substantial improvements in image quality, with better contrast, sharper details, and higher scores in the quality metrics compared to traditional methods, making it a robust and efficient solution for high-quality image processing tasks.