Ocean oil spills pose a severe threat to the marine environment. This research addresses the significant challenge of detecting low-contrast oil spills on the sea surface, a problem exacerbated by the presence of specular reflections from sunlight in visible light images and thermal noise in infrared images. A novel environment-adaptive dual-light image enhancement framework is proposed for marine oil spill detection. Firstly, an improved Criminisi sun glint inpainting algorithm is proposed to eliminate the effects of sun glint regions in visible light images. As the oil spill regions in infrared images can be distorted by thermal noise and background interference, a novel Difference of Gaussian Weighted Guided Image Filtering (DoGWGIF) enhancement algorithm is then created to enhance local detail clarity and significantly increase the overall contrast of the oil spill targets, thereby improving the oil spill detection ability in the infrared images. The proposed algorithms were validated through experiments conducted on marine sun glint regions and low-contrast infrared areas, demonstrating their effectiveness. The performance index MIoU went up 1.64 % and 0.54 % for visible light images and infrared images, respectively.

中文翻译：

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一种新型环境自适应双光图像增强框架的海洋溢油检测


海洋漏油对海洋环境构成严重威胁。这项研究解决了检测海面上低对比度漏油的重大挑战，可见光图像中存在来自太阳光的镜面反射和红外图像中存在的热噪声加剧了这一问题。提出了一种新颖的环境自适应双光图像增强框架，用于海洋溢油检测。首先，提出了一种改进的 Criminisi 太阳闪光修复算法，以消除可见光图像中太阳闪光区域的影响。由于红外图像中的溢油区域会受到热噪声和背景干扰的扭曲，因此提出了一种新的高斯加权导引图像滤波差值 （DoGWGIF） 增强算法，以增强局部细节清晰度，显著提高溢油目标的整体对比度，从而提高红外图像中的溢油检测能力。通过在海洋太阳闪光区域和低对比度红外区域进行的实验验证了所提出的算法，证明了它们的有效性。可见光图像和红外图像的性能指数 MIoU 分别上升了 1.64 % 和 0.54 %。