基于边缘优先填充的自适应深度图像修复方法

Adaptive Depth Image Inpainting Method Based on Edge-first Filling

针对传统深度图像空洞修复方法引起的物体边缘扭曲、模糊以及修复较大空洞速度缓慢的问题,提出了一种基于边缘优先填充的自适应深度图像修复方法。该方法利用多通道检测提取RGBD图像边缘,经过去除空洞虚假边缘和无用细节信息处理,生成物体的显著性边缘;将此边缘引入到图像修复过程中,优先填充空洞区域的边缘位置,有效解决边缘模糊虚化问题,使修复后的深度图像边缘结构清晰;在曲率驱动扩散(Curvature Driven Diffusion, CDD)模型的扩散项中引入梯度引导函数,使模型在空洞的平坦区域和边缘区域自适应地选择不同的扩散方向和扩散强度,实现对较大空洞区域的准确填充。实验结果表明,所提方法在RGBZ数据集上与其他方法进行比较,峰值信噪比(Peak Signal to Noise Ratio, PSNR)和平均结构相似性(Mean Structural Similarity, MSSIM)分别提高了8~13 dB、0.009 9~0.021 4,在提高迭代效率的同时有效修复了较大空洞,保持了较为清晰完整的物体边缘轮廓信息。

To solve the problems of object edge distortion and blur caused by traditional depth image hole inpainting methods and the slow speed of repairing large holes,an adaptive depth image inpainting method based on edge-first filling is proposed.Firstly,multi-channel detection is used to extract the edges of the Red,Green,Blue and Depth(RGBD)image,and after removing the false edges of the holes and useless detail information,a salient edge of the object is generated;then,this edge is introduced into the image inpainting process,and the edge position of the hole area is filled first to effectively solve the problem of edge blurring and make the edge structure of the repaired depth image clear;finally,a gradient guidance function is introduced into the diffusion term of the Curvature Driven Diffusion(CDD)model,allowing the model to adaptively select different diffusion directions and diffusion strengths in the flat area and edge area of the hole to achieve accurate filling of large hole areas.Experimental results show that the Peak Signal to Noise Ratio(PSNR)and Mean Structural Similarity(MSSIM)of the proposed method are improved by 8~13 dB and 0.0099~0.0214 respectively compared with other methods on the RGBZ dataset.The proposed method can effectively repair large holes while improving iteration efficiency,and maintain clear and complete object edge contour information.