An ideal image is desirable to faithfully represent the real-world scene. However, the observed images from imaging system are typically involved in the illumination of light. As the human visual system (HVS) is capable of perceiving identical color with spatially varying illumination, retinex theory is established to probe the rationale of HVS on perceiving color. In the realm of image processing, the retinex models are devoted to diminishing illumination effects from observed images. In this paper, following the recent work by Ng and Wang (SIAM J. Imaging Sci. 4:345-356, 2011), we develop a parallel operator splitting algorithm tailored for the constrained total-variation retinex model, in which all the resulting subproblems admit closed form solutions or can be tractably solved by some subroutines without any internally nested iterations. The global convergence of the novel algorithm is analysed on the perspective of variational inequality in optimization community. Preliminary numerical simulations demonstrate the promising performance of the proposed algorithm.

中文翻译：

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求解约束总变化量的并行算子拆分算法

需要一个理想的图像来忠实地表示真实世界的场景。然而，从成像系统观察到的图像通常涉及光的照明。由于人类视觉系统（HVS）能够通过空间变化的照明感知相同的颜色，因此建立了retinex理论以探讨HVS在感知颜色方面的原理。在图像处理领域，retinex模型致力于减少观察到的图像的照明效果。在本文中，根据Ng和Wang的最新工作（SIAM J. Imaging Sci。4：345-356，2011），我们开发了一种针对约束总变量retinex模型量身定制的并行算子拆分算法，其中所有结果子问题允许采用封闭形式的解决方案，或者可以由某些子例程轻松解决，而无需任何内部嵌套的迭代。从优化社区的变分不等式的角度分析了新算法的全局收敛性。初步的数值模拟证明了该算法的良好性能。