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Non-uniform low-light image enhancement via non-local similarity decomposition model
Signal Processing: Image Communication ( IF 3.5 ) Pub Date : 2021-01-13 , DOI: 10.1016/j.image.2021.116141
Yahong Wu , Wanru Song , Jieying Zheng , Feng Liu

Many low-light image enhancement methods ignore the characteristic of non-uniform low-light images, thereby causing the enhanced results to lose their naturalness. In this paper, a non-local similarity decomposition model based on the Retinex theory is proposed to obtain high-quality enhanced results for non-uniform low-light images. For the reflectance layer, we explore a weighted L1-norm regularization according to the similarity measure of non-local patches. The similarity measure contains color, contrast and texture information, which are the intrinsic features of images. For the illumination layer, we introduce a weight matrix to constrain its gradient based on the edge information of non-uniform low-light images. By applying these constraints, it is more precise to estimate the reflectance and illumination layers simultaneously. Finally, the decomposed illumination is further adjusted. Furthermore, an alternating direction method of multipliers and preconditioned conjugate gradient are utilized to accelerate the solution of the proposed model. Experimental results demonstrate that our method is better for enhancing non-uniform low-light image both in subjective and objective assessments.



中文翻译:

通过非局部相似度分解模型进行不均匀的弱光图像增强

许多弱光图像增强方法忽略了不均匀的弱光图像的特性,从而导致增强的结果失去其自然性。本文提出了一种基于Retinex理论的非局部相似度分解模型,以获取非均匀弱光图像的高质量增强结果。对于反射层,我们探索加权大号1个-根据非局部补丁的相似性度量进行规范化正则化。相似性度量包含颜色,对比度和纹理信息,它们是图像的固有特征。对于照明层,我们基于不均匀的弱光图像的边缘信息引入权重矩阵来约束其梯度。通过应用这些约束,可以更精确地同时估计反射率和照明层。最后,进一步调节分解后的照明。此外,乘数和预先设定的共轭梯度的交替方向方法被用来加速所提出模型的求解。实验结果表明,我们的方法在主观和客观评估中均能更好地增强不均匀的弱光图像。

更新日期:2021-01-31
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