Local Foreground Removal Disocclusion Filling Method for View Synthesis

被引:5
作者
Liang, Haitao [1 ]
Chen, Xiaodong [1 ]
Xu, Huaiyuan [1 ]
Ren, Siyu [1 ]
Cai, Huaiyu [1 ]
Wang, Yi [1 ]
机构
[1] Tianjin Univ, Key Lab Optoelect Informat Technol, Minist Educ, Sch Precis Instrument & Optoelect Engn, Tianjin 300072, Peoples R China
关键词
Free viewpoint video; depth-image-based rendering; disocclusion filling; local foreground removal; view synthesis; VIDEO; COMPRESSION; GENERATION; MULTIVIEW; DIBR;
D O I
10.1109/ACCESS.2020.3036053
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
View synthesis is an effective method to generate the contents of multiple views based on a limited number of reference views, which can be used in 2D to 3D conversion, free viewpoint video and multiview video rendering. Depth-image-based rendering (DIBR) is a practical technique to generate virtual view by using a 2D reference view and its depth image. However, a critical problem in DIBR process is that disocclusions might be produced in the synthesized image because the background occluded by the foreground objects in the reference view may be exposed in the virtual view. In this paper, a local foreground removal method is proposed for disocclusion filling. Morphology-based depth image preprocessing is performed before DIBR, aiming to correct the depth value of the ghosts and remove ghost artifacts. In the synthesized virtual image, pixels on the disocclusion edge are identified and classified. Then they are positioned in the reference image by inverse 3D warping. Local foreground regions that occlude the corresponding background are removed from both the reference image and its depth image based on the disocclusion edge pixels. Removed region is filled with surrounding background contents, and depth information is used in this process to prevent foreground penetration. The predicted background contents are warped to the disocclusion region, thereby achieving the hole filling. Experimental results show that the proposed method performs better than the other methods in disocclusion filling, and improves the subjective and objective quality of the synthesized view. In the evaluation results of PSNR, SSIM, FSIMc and VSI, our method improves by 0.32-2.43dB, 0.0036-0.0155, 0.0041-0.0198 and 0.0012-0.0057 respectively compared with competitive methods.
引用
收藏
页码:201286 / 201299
页数:14
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