Adaptive Image Enhancement Using Quantum Genetic Algorithm for Parameter Optimization

In this document, we present an adaptive image enhancement approach utilizing quantum genetic algorithms for parameter optimization. The quantum genetic algorithm (QGA) represents an advanced optimization technique that merges quantum computing principles with genetic algorithm operations. Implementation typically involves encoding parameters as quantum bits (qubits) using superposition states, where each qubit population evolves through quantum rotation gates and crossover operations. Key functions include quantum measurement for collapsing qubits into binary solutions and fitness evaluation based on image quality metrics like entropy or contrast measure. By employing QGA, the system autonomously adjusts enhancement parameters such as gamma correction values or histogram equalization thresholds to achieve optimal visual quality across diverse scenarios. This adaptive enhancement technology significantly improves image contrast, sharpness, and detail preservation, resulting in more realistic and visually appealing outputs. The algorithm's parallel exploration capability enables efficient global optimization, making QGA-based parameter tuning a highly effective method for achieving substantial improvements and innovations in digital image processing applications.