Abstract
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.
Abstract (translated)
水下光学成像受到光吸收、散射和色彩失真的严重影响，这阻碍了可见度和准确的图像分析。本文提出了一种自适应增强框架，该框架集成了光照补偿、多域过滤和动态颜色校正。一种结合CLAHE（对比限制自适应直方图均衡）、Gamma矫正和Retinex技术的混合光照补偿策略增强了可见性。两阶段滤波过程包括空间领域（高斯、双边、导向）和频率领域（傅里叶、小波）方法，有效地减少噪声同时保留细节。为了校正颜色失真，自适应色彩补偿(ACC)模型估计光谱衰减并确定水体类型，从而动态结合RCP（反射色偏置）、DCP（直接颜色偏置）和MUDCP（多域统一动态色彩处理）。最后，感知导向的色彩平衡机制确保了自然的颜色恢复。在基准数据集上的实验结果表明，该框架在对比度增强、颜色校正和结构保留方面优于现有的最先进的方法，使其适用于水下成像应用，并表现出色的鲁棒性。
URL
https://arxiv.org/abs/2503.03640
