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5 March 2018 Variational method based on Retinex with double-norm hybrid constraints for uneven illumination correction
Shuo Li, Hui Wang, Liyong Wang, Xiangzhou Yu, Le Yang
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Journal of Applied Remote Sensing, Vol. 12, Issue 1, 015017 (March 2018). https://doi.org/10.1117/1.JRS.12.015017
Abstract
The uneven illumination phenomenon reduces the quality of remote sensing image and causes interference in the subsequent processing and applications. A variational method based on Retinex with double-norm hybrid constraints for uneven illumination correction is proposed. The L1 norm and the L2 norm are adopted to constrain the textures and details of reflectance image and the smoothness of the illumination image, respectively. The problem of separating the illumination image from the reflectance image is transformed into the optimal solution of the variational model. In order to accelerate the solution, the split Bregman method is used to decompose the variational model into three subproblems, which are calculated by alternate iteration. Two groups of experiments are implemented on two synthetic images and three real remote sensing images. Compared with the variational Retinex method with single-norm constraint and the Mask method, the proposed method performs better in both visual evaluation and quantitative measurements. The proposed method can effectively eliminate the uneven illumination while maintaining the textures and details of the remote sensing image. Moreover, the proposed method using split Bregman method is more than 10 times faster than the method with the steepest descent method.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2018/$25.00 © 2018 SPIE
Citation Download Citation
Shuo Li, Hui Wang, Liyong Wang, Xiangzhou Yu, and Le Yang "Variational method based on Retinex with double-norm hybrid constraints for uneven illumination correction," Journal of Applied Remote Sensing 12(1), 015017 (5 March 2018). https://doi.org/10.1117/1.JRS.12.015017
Received: 2 October 2017; Accepted: 14 February 2018; Published: 5 March 2018
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KEYWORDS
Reflectivity
Remote sensing
Image quality
Image analysis
Visualization
Image processing
Lithium
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