针对从二维彩色图像中恢复深度信息的问题,提出一种基于视觉词典的深度图生成算法。采用基于数据驱动的方法,从包含深度图的深度图像库中找出图像中各种空间结构对应的深度信息,得到由空间结构相似的图像块组成的初始视觉单词;采用难例挖掘方法找到视觉单词的难例负样本,更新视觉单词分类器,获得最优的分类效果;利用视觉单词分类器和视觉单词组成的视觉词典对目标图像进行多尺度检测,得到对应的深度图并进行边缘保持平滑滤波。实验结果表明,该算法生成的深度图符合目标图像的深度变化,在主观视觉效果和各种客观评价指标上都有显著提高。

In order to recover depth information from two-dimensional color image, a visual-dictionary-based depth map generation algorithm is proposed. A data-driven method is used to find depth information of various spatial structures from depth map library, so as to obtain initial visual words which consist of image patches with similar structure. Hard example mining method is used to find hard negative examples of visual word, and visual word classifier is updated to get best classification result. Visual dictionary composed of visual word classifiers and visual words is used to detect target image at multiple scales to get corresponding depth map, to which edge-preserving smoothing filter will be applied. Experimental results show that depth maps generated by the proposed algorithm match depth change of target images, and has a good improvement in both subjective visual effects and objective evaluation indexes.