A sparsity-promoting image decomposition model for depth recovery
作者：
Highlights：
• We propose a general depth recovery model from signal decomposition perspective to better portray the 2D depth signal intrinsically.
• We proposed a new non-convex penalty to promote the prior sparsity and simultaneously maintain the convexity of the whole model for each variable.
• We introduce an iterative reweighted strategy to deal with the depth-color inconsistent problem and locate the depth boundaries.
• Experimental results demonstrate that the proposed method achieves promising performance in terms of recovery accuracy and running time
摘要
•We propose a general depth recovery model from signal decomposition perspective to better portray the 2D depth signal intrinsically.•We proposed a new non-convex penalty to promote the prior sparsity and simultaneously maintain the convexity of the whole model for each variable.•We introduce an iterative reweighted strategy to deal with the depth-color inconsistent problem and locate the depth boundaries.•Experimental results demonstrate that the proposed method achieves promising performance in terms of recovery accuracy and running time
论文关键词：Image decomposition,Depth recovery,Depth discontinuities,Depth cameras
论文评审过程：Received 13 June 2019, Revised 24 December 2019, Accepted 12 June 2020, Available online 13 June 2020, Version of Record 23 June 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107506
