Quantum Fourier Transform-Based Algorithms for Underwater Image Contrast and Color Correction

Light of long wavelengths is absorbed almost immediately with depth, while remaining photons are scattered by suspended particles, producing images that are bright, color‑distorted, and structurally blurred. Underwater imaging is therefore a grand challenge across diverse marine environments. Existing enhancement methods fail to balance image fidelity and speed or depend on large, environment‑specific training sets that lack generalization. We propose a frequency‑based quantum‑classical pipeline that uses the Quantum Fourier Transform (QFT) to optimize underwater images without extensive data. The luminance and chroma channels are encoded into quantum states; QFT reveals the spectral content. In the quantum frequency domain, we suppress low‑frequency illumination artifacts and enhance high‑frequency edges and fine details. The modified spectrum is decoded back into classical space in a manner compatible with Noisy Intermediate‑Scale Quantum (NISQ) devices. Experiments on standard underwater datasets show significant improvements in visibility, color balance, and edge sharpness compared to traditional Fourier methods and deep‑learning models. This work demonstrates that quantum spectral processing is an efficient, multi‑productive tool for underwater image enhancement, providing substantial gains in underwater visual perception notably, and for marine navigation and research, in challenging visibility conditions for autonomous underwater vehicles.