针对从单目红外图像中恢复深度信息的问题，提出了一种基于深层卷积神经网络（DCNN）的深度估计方法。用劳斯掩膜和梯度检测器分别提取不同尺度下红外图像的纹理能量与纹理梯度，并将这两种纹理信息作为红外图像的第一种特征；提取图像中像元及其邻域的灰度值,以及统计其灰度直方图作为另外两种特征；分别用三种特征和深度信息标签训练DCNN，得到三种训练后的DCNN分别对单目红外图像进行深度估计。实验结果表明，相比较另外两种特征，用纹理信息训练的DCNN能够更有效地估计深度，并且优于现有的估计方法，尤其能较好地表现局部场景的深度变化。

In order to recover depth information from monocular infrared image, a depth estimation algorithm based on novel deep convolutional neural networks (DCNN) is proposed. The texture energy and texture gradient of infrared images are extracted by using Laws′ masks and the gradient detector at different scales. These two types of texture information are considered as the first kind of features. The selected gray values and their statistical histogram in specific areas are considered as another two kinds of features. The DCNN are trained on these three kinds of features with the corresponding depth labels respectively. The trained DCNN are then utilized to estimate the depths of testing monocular infrared images respectively. Experimental results show that compared with other methods, the DCNN trained by texture information can estimate the depth much better than those of the existing methods, especially in the depth changes of local scenes.