基于光场图像序列的自适应权值块匹配深度估计算法

现有的深度估计算法中,针对光场序列图像进行深度估计时,在图像亮度变化较大和弱纹理区域,其匹配效果较差,鲁棒性较低.针对这些问题,本文提出了一种基于CIELab颜色空间的自适应权值块匹配算法.由于彩色图像RGB颜色空间中颜色差异匹配影响因素较多,本算法转换到CIELab空间进行颜色相似性匹配来计算权重值,然后结合梯度和距离计算匹配图像和待匹配图像中匹配块得到综合权重值,最后根据极平面图像(EPI)的线性特性对图像序列中匹配图像和待匹配图像块进行匹配计算,求得深度图.经过仿真验证,本文算法能够较好的估计场景的深度信息,精度上有较大的提升,明显优于以往的深度估计算法,可以广泛使用.

Adaptive Weight Block Matching Depth Estimation Algorithm Based on Light Field Image Sequence

In the existing depth estimation algorithm, when the depth estimation is performed on the image of the light field sequence, the matching effect is poor and the robustness is low when the image brightness changes and in the weak texture region. Aiming to solve these problems, this study proposes an adaptive weight block matching algorithm based on CIELab color space. Since the color difference matching in color image RGB color space has many influencing factors, the algorithm converts to CIELab space for color similarity matching to calculate the weight value, and then combines the gradient and distance to calculate the matching image and the matching block in the image to be matched to obtain the comprehensive weight value. Finally, according to the linear characteristics of the Epipolar Plane Image (EPI), the matching image and the image block to be matched in the image sequence are matched and calculated, and the depth map is obtained. After simulation, the proposed algorithm can estimate the depth information of the scene better, and the accuracy is greatly improved. It is obviously superior to the previous depth estimation algorithm and can be widely used.