基于超像素分割的单幅散焦图像深度恢复方法

Depth Estimation from Single Defocused Image Based on Superpixel Segmentation

现有的单幅散焦图像深度恢复算法大多存在算法复杂,对图像边缘、复杂纹理及阴影区域恢复效果差等问题。提出一种基于超像素分割的单幅散焦图像的深度恢复方法。首先将原始图像分割成若干超像素模块,然后根据图像中边缘处像素的散焦模糊量求得各超像素模块的散焦模糊量,以获得超像素级别的稀疏深度图,再对所求出的稀疏深度图进行优化处理,最后恢复出真实准确的全景深度图。该算法不仅可以将误差降低到最小,而且可以简化边缘散焦模糊量向全局扩展的过程。在真实数据上的仿真实验表明,该方法不仅耗时短,而且可以有效改进边缘不明显、纹理复杂以及存在阴影区域的深度恢复效果。

Existing depth recovery algorithms for single defocused image may be complex and not effective at edge locations,textured areas and the regions with soft shadows.This paper proposes a depth estimation from single defocused image based on superpixel segmentation.At first,original image is segmented to some superpixel modules,and then the blur of these modules can be computed based on the blur of pixels at edge locations.In so doing,sparse depth maps at superpixel level will be obtained and optimized.At last,truthful and accurate depth maps may be generated.The proposed method can decrease the error to the minimum and simplify the extension process of blurs from edges to all image pixels.The experimental results on real data demonstrate that the proposed method is not only faster,but can improve depth maps effectively,especially for edge locations,textured regions and soft shadows as well.