Binary quantum genetic algorithm with diversity-guided phase transitions for CT image enhancement

Abstract Clinical CT image enhancement requires a precise balance between noise suppression and the preservation of anatomically relevant structures. Conventional genetic algorithms address this as a global optimisation problem, but they typically rely on static rotation schedules that are insensitive to the population’s collective convergence state, leading to premature diversity collapse in high-dimensional parameter spaces. To address this limitation, we propose a Binary Quantum Genetic Algorithm with Diversity-Guided Phase Transitions (BQGA). The algorithm encodes five CT enhancement parameters, including window center, window width, contrast gain, noise suppression, and edge retention, as quantum bits on the Bloch sphere. At each generation, a composite diversity score is computed from four complementary population metrics and mapped to one of three thermodynamic phases (plasma, liquid, crystal) via a hysteresis-buffered threshold rule. The active phase governs the quantum walk step size, mutation rate, and coin operator, providing closed-loop regulation of the balance between exploration and exploitation without manual schedule design. A gradient-aware mutation weight additionally modulates the mutation strength according to the overall edge density of the volume, limiting the introduction of halo artefacts during local refinement. BQGA is evaluated on abdominal and lesion-bearing CT volumes from the CHAOS and LiTS2017 datasets against both metaheuristic optimisers (QGA, QPSO, HHO, MPA) and learning-based methods (Zero-DCE and DPM). It attains the highest mean PSNR, SSIM, and UQI, with the margin widening on low-contrast, high-noise volumes, and non-parametric tests indicate that these gains are statistically significant for most fidelity metrics. Convergence and stability analyses, together with a downstream Chan–Vese segmentation task, further support the reliability and practical utility of the proposed method. Ablation analysis confirms that the phase-transition mechanism accounts for the majority of the performance gain, and that both the multi-dimensional diversity signal and the gradient-aware weighting contribute independently.