A Reversible Grayscale Method Based on Bit-Field Multi-Channel Fusion Encoding

Grayscale image representation is widely adopted to reduce computational complexity, optimize storage, and enhance transmission efficiency. However, in many applications, restoring the original color information from grayscale images is highly desirable. Existing methods, often suffer from high computational costs, limited interpretability, and dependence on training data. To address these limitations, we propose a novel non-learning-based approach that encodes RGB color information into a 16-bit grayscale image by strategically embedding HSV components, allowing high-fidelity recolorization while remaining lightweight and hardware-friendly. Experimental results validate the effectiveness of our approach in balancing reconstruction accuracy, computational efficiency, and practicality. The proposed method offers a promising alternative to traditional and neural network-based solutions, particularly in resource-constrained environments.