A high-quality underwater image is essential to many industrial and academic applications in the field of image processing and analysis. Unfortunately, underwater images frequently demonstrate poor visual quality of low contrast, blurring, darkness, and color diminishing. This paper develops a new underwater image restoration framework that consists of four major phases: color correction, local contrast enhancement, haze diminution, and global contrast enhancement. A self-adaptive mechanism is designed to guide the image to either processing route based on a red deficiency measure. In the color correction phase, the histogram in each RGB channel is transformed for balancing the image color. An adaptive histogram equalization method is exploited to enhance the local contrast in the CIE-Lab color space. The dark channel prior haze removal scheme is modified for dehazing in the haze diminution phase. Finally, a histogram stretching method is applied in the HSI color space to make the image more natural. A wide variety of underwater images with various scenarios were employed to evaluate this new restoration algorithm. Experimental results demonstrated the effectiveness of our image restoration scheme as compared with state-of-the-art methods. It was suggested that our framework dramatically eliminated the haze and improved visual interpretation of underwater images.

中文翻译：

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用于改善图形质量的自适应单一水下图像恢复算法

对于图像处理和分析领域中的许多工业和学术应用而言，高质量的水下图像至关重要。不幸的是，水下图像经常表现出低对比度，模糊，黑暗和色彩减弱的不良视觉质量。本文开发了一个新的水下图像恢复框架，该框架包括四个主要阶段：颜色校正，局部对比度增强，雾度减小和全局对比度增强。一种自适应机制旨在根据红色不足量度将图像引导至任一处理路径。在色彩校正阶段，对每个RGB通道中的直方图进行转换以平衡图像色彩。利用自适应直方图均衡方法来增强CIE-Lab颜色空间中的局部对比度。修改暗通道先前的除雾方案，以在雾度减小阶段除雾。最后，在HSI颜色空间中应用直方图拉伸方法以使图像更自然。使用具有各种场景的各种水下图像来评估此新的恢复算法。实验结果表明，与最新方法相比，我们的图像恢复方案是有效的。有人建议，我们的框架可以极大地消除雾霾，改善水下图像的视觉解释。实验结果表明，与最新方法相比，我们的图像恢复方案是有效的。有人建议，我们的框架可以极大地消除雾霾，并改善水下图像的视觉解释。实验结果表明，与最新方法相比，我们的图像恢复方案是有效的。有人建议，我们的框架可以极大地消除雾霾，改善水下图像的视觉解释。