Depth information plays a crucial role in autonomous systems for environmental perception and robot state estimation. With the rapid development of deep neural network technology, depth estimation has been extensively studied and shown potential for practical applications. However, in particularly challenging environments such as low-light and noisy underwater conditions, direct application of machine learning models may not yield the desired results. Therefore, in this paper, we present an approach to enhance underwater image quality to improve depth estimation effectiveness. First, underwater images are processed through methods such as color compensation, brightness equalization, and enhancement of contrast and sharpness of objects in the image. Next, we perform depth estimation using the Udepth model on the enhanced images. Finally, the results are evaluated and presented to verify the effectiveness and accuracy of the enhanced depth image quality approach for underwater robots.

翻译：深度信息在自主系统的环境感知与机器人状态估计中起着至关重要的作用。随着深度神经网络技术的快速发展，深度估计已得到广泛研究并展现出实际应用的潜力。然而，在诸如低光照和噪声干扰的水下等极具挑战性的环境中，直接应用机器学习模型可能无法获得理想结果。因此，本文提出一种提升水下图像质量以改善深度估计效果的方法。首先，通过色彩补偿、亮度均衡以及增强图像中物体的对比度和锐度等方法对水下图像进行处理。接着，我们在增强后的图像上使用Udepth模型进行深度估计。最后，对结果进行评估和展示，以验证所提出的增强深度图像质量方法对于水下机器人的有效性和准确性。