Quantitative Evaluation of Color Enhancement Methods for Underwater Photogrammetry in Very Shallow Water: a Case Study

A. Calantropio, F. Chiabrando, F. Menna, E. Nocerino
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Abstract

Abstract. Underwater photogrammetry is often hampered by chromatic aberration, leading to degraded 2D and 3D products. This study investigates the effectiveness of various color enhancement methods in addressing these challenges.Theoretical considerations indicate that light penetration depth varies inversely with wavelength, causing underwater images to exhibit a blue or green cast with increasing depth. Color enhancement techniques can restore natural colors by compensating for this spectral attenuation. Additionally, scattering, caused by light reflected by particles in the water, can introduce haze into underwater images. Color enhancement can mitigate scatter and improve image clarity. In this contribution, to quantitatively evaluate color enhancement methods, we compare original images with images processed using gray-world assumption methods and physical methods that account for the physical properties of light underwater. Using artificial intelligence (AI) for underwater image color enhancement, a data-driven approach was also employed. These methods were applied to a case study concerning a Roman Navis Lapidaria shipwreck carrying five monumental cipollino marble columns at a depth of 4.5 meters in the Porto Cesareo Marine Protected Area (Italy). These methods were compared quantitatively and qualitatively, and the results are presented and discussed.
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对极浅水域水下摄影测量色彩增强方法的定量评估:案例研究
摘要水下摄影测量经常受到色差的影响,导致二维和三维产品的质量下降。理论研究表明,光的穿透深度与波长成反比,导致水下图像随着深度的增加而呈现蓝色或绿色。色彩增强技术可以通过补偿这种光谱衰减来还原自然色彩。此外,水中颗粒反射的光线所产生的散射也会给水下图像带来雾度。色彩增强可以减少散射,提高图像清晰度。在本文中,为了定量评估色彩增强方法,我们将原始图像与使用灰度世界假设方法和考虑到水下光的物理特性的物理方法处理的图像进行了比较。我们还采用了人工智能(AI)进行水下图像色彩增强,这是一种数据驱动的方法。这些方法被应用于一项案例研究,该案例涉及意大利切萨雷奥 港海洋保护区 4.5 米深处的一艘罗马 Navis Lapidaria 沉船,船上载有五根具有纪念意义的 cipollino 大理石圆柱。对这些方法进行了定量和定性比较,并对结果进行了介绍和讨论。
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