Title :
Shadow removal for light field images
Author :
Yan Zhou ; Huiwen Guo ; Guoyuan Liang ; Xinyu Wu
Author_Institution :
Guangdong Provincial Key Lab. of Robot. & Intell. Syst., Shenzhen Inst. of Adv. Technol., Shenzhen, China
Abstract :
In this paper, we present an automatic method to remove shadows in light field images. Taking into account the internal structure of the light field data, depth map of the captured scene is extracted to calculate the surface normal. Using nonlocal matching by combining chromaticity, normal and spatial location information in an anisotropic window, the shadow confidence of each pixel is established. For effectively utilizing the prior knowledge, MRF (Markov random fields) is introduced to obtain the shadow label of each pixel iteratively. Once obtained the shadow labels of pixels, inpainting with an energy minimize framework is used to remove shadows. The experimental results on real data demonstrate good performance of this algorithm.
Keywords :
Markov processes; image matching; MRF; Markov random fields; anisotropic window; automatic shadow removal method; energy minimize framework; light field images; nonlocal matching method; normal information; shadow labels; spatial location information; Cameras; Computational modeling; Computer vision; Image color analysis; Lighting; Mathematical model; Minimization; MRF; depth map; light field images; nonlocal matching; shadow removal;
Conference_Titel :
Information and Automation (ICIA), 2014 IEEE International Conference on
Conference_Location :
Hailar
DOI :
10.1109/ICInfA.2014.6932830