QUANTUM INSPIRED GENETIC ALGORITHM FOR BI-LEVEL THRESHOLDING OF GRAY-SCALE IMAGES

Thresholding is the primitive step in the process of image segmentation. Finding the optimal threshold for satellite images with reduced computation time and resources is still a challenging task. In this paper, we propose a Grey-Level Co-occurrence Matrix based Quantum Inspired Genetic Algorithm (QGA-GLCM) for bi-level thresholding of gray-scale images (natural and satellite). In this paper, QGA was used to find the optimal threshold. The results are compared with four different variants of Differential Evolution (DE) meta-heuristic algorithms, namely- DE-Otsu, DE-Kapur, DE-Tsali's, DE-GLCM, and three different variants of QGA, namely- QGAOtsu, QGA-Kapur, QGA-Tsali's. Intensity value from image pixel is the only information used by Otsu, Tsali's and Kapur for thresholding and are highly affected by noise. The main objective of this paper was a) To have a binary threshold for images corrupted with noise by bringing in spatial context b) To reduce the computational complexity and time for generating a threshold. Performance evaluators viz., CPU time, Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) were used for quantitative assessment of partitioned images. From this study we observed that our proposed technique, QGA-GLCM is a) very good at producing a diverse population b) ten times faster than its classical counterparts c) generates better threshold for images corrupted by noise. In general, the threshold values generated by QGA and its variants are better than its classical counterparts. The results clearly show that exploration and exploitation capability of QGA is superior to DE for all variants. QGA-GLCM can be an effective technique to generate thresholds both in terms of computational speed and time.