An Adaptive Underwater Image Enhancement Framework via Multi-Domain Fusion and Color Compensation
This work addresses visibility and analysis issues in underwater optical imaging, which is incremental as it combines existing methods into a novel framework.
The paper tackles the problem of degraded underwater images due to light absorption, scattering, and color distortion by proposing an adaptive enhancement framework that integrates illumination compensation, multi-domain filtering, and dynamic color correction, achieving superior performance over state-of-the-art methods in contrast enhancement, color correction, and structural preservation on benchmark datasets.
Underwater optical imaging is severely degraded by light absorption, scattering, and color distortion, hindering visibility and accurate image analysis. This paper presents an adaptive enhancement framework integrating illumination compensation, multi-domain filtering, and dynamic color correction. A hybrid illumination compensation strategy combining CLAHE, Gamma correction, and Retinex enhances visibility. A two-stage filtering process, including spatial-domain (Gaussian, Bilateral, Guided) and frequency-domain (Fourier, Wavelet) methods, effectively reduces noise while preserving details. To correct color distortion, an adaptive color compensation (ACC) model estimates spectral attenuation and water type to combine RCP, DCP, and MUDCP dynamically. Finally, a perceptually guided color balance mechanism ensures natural color restoration. Experimental results on benchmark datasets demonstrate superior performance over state-of-the-art methods in contrast enhancement, color correction, and structural preservation, making the framework robust for underwater imaging applications.