Intrinsic images using optimization

  • Authors:

  • Jianbing Shen; Xiaoshan Yang; Yunde Jia; Xuelong Li

  • Affiliations:

  • Beijing Lab. of Intell. Inf. Technol., Beijing Inst. of Technol., Beijing, China;Beijing Lab. of Intell. Inf. Technol., Beijing Inst. of Technol., Beijing, China;Beijing Lab. of Intell. Inf. Technol., Beijing Inst. of Technol., Beijing, China;State Key Lab. of Transient Opt. & Photonics, Chinese Acad. of Sci., Xi'an, China

  • Venue:
  • CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
  • Year:
  • 2011

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Abstract

In this paper, we present a novel intrinsic image recovery approach using optimization. Our approach is based on the assumption of in a local window in natural images. Our method adopts a premise that neighboring pixels in a local window of a single image having similar intensity values should have similar reflectance values. Thus the intrinsic image decomposition is formulated by optimizing an energy function with adding a weighting constraint to the local image properties. In order to improve the intrinsic image extraction results, we specify local constrain cues by integrating the user strokes in our energy formulation, including constant-reflectance, constant-illumination and fixed-illumination brushes. Our experimental results demonstrate that our approach achieves a better recovery of intrinsic reflectance and illumination components than by previous approaches.