Image depth estimation from compressed sensing theory

Guangdong Du; Cheng Zhang; Hong Cheng; Yan Liu; Sui Wei

doi:10.1117/12.2072454

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24 November 2014 Image depth estimation from compressed sensing theory

Guangdong Du, Cheng Zhang, Hong Cheng, Yan Liu, Sui Wei

Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 930125 (2014) https://doi.org/10.1117/12.2072454
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

Abstract

Depth information of the image is really necessary information to reconstruct a 3-dimensional object. The classical methods of depth estimation are generally divided into two categories: active and passive methods. The active methods requires the additional lighting equipment, passive methods also have a series of problems .They require a plurality of images obtained by capturing a plurality of viewpoints , and determine the locating occlusion boundary , etc., and hence the depth estimation has been a challenging problem in the research field of computer vision.1 Because of the depth information of the image has a natural sparse features, this paper uses a passive approach, the signal of sparse priori based on compressed sensing theory is used to estimate the depth of the image, without capturing multiple images, using a single input image can obtain a high quality depth map. Experimental results show that the depth map obtaining by our method, compared to the classical passive method, the contour sharpness, the depth of detail information and the robustness of noise are more advantages. The method also can be applied to re-focus the defocused images, and automatic scene segmentation and other issues, ultimately may have broad application prospects in the reconstruction of true 3-dimensional objects.

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Guangdong Du, Cheng Zhang, Hong Cheng, Yan Liu, and Sui Wei "Image depth estimation from compressed sensing theory", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 930125 (24 November 2014); https://doi.org/10.1117/12.2072454

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