翻译成英文：本文提出了一种新的信息熵引导的深度修复策略该策略可以有效地解决从深度传感器获取的原始深度图像中缺失深度信息的问题。首先原始深度图和彩色图像被进行预处理 为后续任务提供连通孔洞图和灰度图。然后信息熵的概念被引入通过对有效邻域内的深度信息进行评估应该首先进行填充的无效点被确定。接下来根据彩色和梯度信息的引导来预测无效点的深度值。在这一步中彩色信息保证了预测的准确性并利用梯度信息来进一步提高

This article proposes a new entropy-guided deep repair strategy that effectively addresses the issue of missing depth information in raw depth images acquired from depth sensors. Firstly, the raw depth and color images are preprocessed to provide a connected hole map and grayscale image for subsequent tasks. Then, the concept of information entropy is introduced to identify invalid points that should be filled first by evaluating depth information in effective neighborhoods. Next, the depth values of invalid points are predicted based on color and gradient information guidance. In this step, color information ensures the accuracy of prediction, and gradient information is utilized to further improve the precision of repair. In this way, missing information in the raw depth image is effectively repaired. Finally, comparative experiments on the Middlebury dataset demonstrate that the proposed method exhibits better robustness and accuracy than other intelligent methods, providing new ideas and methods for relevant research in depth image processing.