Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: High-quality depth recovery from RGB-D data has received increasingly more attention in recent years due to their wide applications from depth-based image rendering to three-dimensional imaging and video. Sharp contrast beten high-quality color images and low-quality depth maps presents severe challenges to the development of color-guided depth recovery techniques. Previous works have emphasized either  locally  varying characteristics of color-depth dependence or  nonlocal  similarities around the discontinuities of the scene geometry. Therefore, it is desirable to exploit both local and nonlocal structural constraints for optimizing the performance of color-guided depth recovery. In this work,  propose a unified variational approach via joint local and nonlocal regularization. The local regularization term consists of two complementary parts—one characterizing the color-depth dependence in the gradient domain and the other in the spatial domain; nonlocal regularization involves a low-rank constraint suitable for large-scale depth discontinuities. Extensive experimental results are reported to show that our approach outperforms several existing state-of-the-art depth recovery methods on both synthetic and real-world data sets.
['Weisheng Dong', 'Guangming Shi', 'Xin Li', 'Kefan Peng', 'Jinjian Wu', 'Zhenhua Guo']
I am writing a research paper on a idea Color-Guided Depth Recovery via Joint Local Structural and Nonlocal Low-Rank Regularization
[{"from": "human", "value": "I am writing a research paper on a idea Color-Guided Depth Recovery via Joint Local Structural and Nonlocal Low-Rank Regularization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: High-quality depth recovery from RGB-D data has received increasingly more attention in recent years due to their wide applications from depth-based image rendering to three-dimensional imaging and video. Sharp contrast beten high-quality color images and low-quality depth maps presents severe challenges to the development of color-guided depth recovery techniques. Previous works have emphasized either  locally  varying characteristics of color-depth dependence or  nonlocal  similarities around the discontinuities of the scene geometry. Therefore, it is desirable to exploit both local and nonlocal structural constraints for optimizing the performance of color-guided depth recovery. In this work,  propose a unified variational approach via joint local and nonlocal regularization. The local regularization term consists of two complementary parts\u2014one characterizing the color-depth dependence in the gradient domain and the other in the spatial domain; nonlocal regularization involves a low-rank constraint suitable for large-scale depth discontinuities. Extensive experimental results are reported to show that our approach outperforms several existing state-of-the-art depth recovery methods on both synthetic and real-world data sets."}]