基于边缘优先填充的自适应深度图像修复方法OA

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 0.009 9～0.021 4 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.