A Joint Intrinsic-Extrinsic Prior Model for Retinex

Bolun Cai, Xianming Xu, Kailing Guo, Kui Jia, Bin Hu, Dacheng Tao; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 4000-4009

We propose a joint intrinsic-extrinsic prior model to estimate both illumination and reflectance from an observed image. The 2D image formed from 3D object in the scene is affected by the intrinsic properties (shape and texture) and the extrinsic property (illumination). Based on a novel structure-preserving measure called local variation deviation, a joint intrinsic-extrinsic prior model is proposed for better prior representation. Better than conventional Retinex models, the proposed model can preserve the structure information by shape prior, estimate the reflectance with fine details by texture prior, and capture the luminous source by illumination prior. Experimental results demonstrate the effectiveness of the proposed method on simulated and real data. Compared with the other Retinex algorithms and state-of-the-art algorithms, the proposed model yields better results on both subjective and objective assessments.