Adaptive Image Enhancement Using Quantum Genetic Algorithm for Parameter Optimization

Quantum Genetic Algorithm (QGA) is an intelligent optimization method that integrates quantum computing principles with traditional genetic algorithms. It efficiently searches for optimal solutions through quantum bit encoding and quantum gate rotation mechanisms. In the field of adaptive image enhancement, this algorithm dynamically adjusts enhancement parameters (such as contrast stretching coefficients, histogram equalization thresholds) to overcome the limitations of traditional methods that rely on manual parameter tuning based on empirical knowledge.

The implementation approach consists of three key phases: Quantum Encoding: Parameters to be optimized (e.g., gamma correction values, filter kernel sizes) are represented using quantum superposition states. A single quantum bit can simultaneously encode multiple parameter states, thereby expanding the search space. In code implementation, this typically involves creating quantum chromosome structures where each gene corresponds to a quantum bit probability amplitude. Cooperative Evolution: Quantum crossover and mutation operations explore parameter combinations. For instance, probabilistic adjustments can be made to local enhancement weights after image partitioning, combined with fitness function evaluations (such as information entropy, edge clarity metrics) to assess enhancement quality. The fitness function calculation would typically involve image quality assessment algorithms like SSIM or PSNR. Dynamic Convergence: Quantum rotation gates guide the search toward optimal solutions. The iteration terminates when structural similarity (SSIM) metrics of enhanced images stabilize, outputting the final adaptive parameter set. The convergence condition can be implemented through threshold-based stopping criteria or generation count limits.

Compared to traditional genetic algorithms, the quantum version significantly improves convergence speed through parallel search capabilities, making it particularly suitable for complex optimization scenarios involving multimodal images (such as medical imaging where simultaneous enhancement of dark area details and suppression of overexposed regions is required). In Python implementations, libraries like NumPy would handle matrix operations for quantum state simulations while OpenCV would process image enhancement operations. Future developments could integrate deep learning to construct hybrid optimization frameworks, further improving robustness through neural network-based feature extraction and quantum-inspired optimization.