光学 精密工程, 2020, 28 (3): 736, 网络出版: 2020-05-12   

多图像融合Retinex用于弱光图像增强

Multi images fusion Retinex for low light image enhancement
作者单位
1 湖北工业大学 机械工程学院, 湖北 武汉 430068
2 现代制造质量工程湖北省重点实验室, 湖北 武汉 430068
摘要
为解决弱光图像增强过程中对比度增强和自然度保持问题, 本文提出一种基于Retinex的多图像自适应加权最小二乘滤波算法。首先, 在图像的每个像素的R, G, B三通道中找到最大亮度值作为该像素的初始照明估计, 根据Retinex理论生成反射图像, 并通过形态学闭合方式调整反射图; 接着, 在初始照明图基础上, 通过Gamma变换和双对数变换方法分别生成全局对比度增强图和局部自然度保持照明图; 随后, 设计一种自适应加权最小二乘滤波融合策略将三幅照明图融合成最终照明估计图; 最后合成上述的最终照明图和调整反射图以获得弱光增强后的图像。实验结果表明, 本文所提出算法的亮度顺序差(LOE)及盲图像质量评价(NIQE)值更低, 可同时降低到4.12和3.25, 较其他方法表现出更好的增强效果。证明了本文算法能有效地增强弱光图像对比度, 同时保持图像自然度。
Abstract
In this paper, a multiple-image fusion enhancement algorithm based on Retinex was proposed to solve the problem of contrast enhancement and naturalness preservation under low-light conditions. First, the maximum value was found in the R, G, and B channels to estimate the brightness of each pixel of the image as an initial illumination estimation. Based on the Retinex theory, the reflection image was generated and adjusted by morphological closing. Furthermore, the global contrast enhancement map and local natural degree keeping illumination map based on the initial illumination map were generated using a gamma transform and a double logarithmic transform, respectively. Subsequently, an adaptive weighted least square filtering fusion strategy was designed to fuse the three illumination images into the final illumination estimation image. Finally, the final illumination image was synthesized, and the reflection image was adjusted to obtain the image after the low-light enhancement. The experimental results indicate that the proposed algorithm has a lower lightness-order-error and natural image quality evaluator value compared to conventional enhancement algorithms. Moreover, the lightness-order-error and natural image quality evaluator values of real natural scenes can be reduced to 4.12 and 3.25, respectively, which yields better enhancement effects than conventional methods. Therefore, the proposed Retinex-based multiple-image algorithm using adaptive weighted least square filtering can effectively enhance the contrast and retain the natural degree of low-light images.

冯维, 吴贵铭, 赵大兴, 刘红帝. 多图像融合Retinex用于弱光图像增强[J]. 光学 精密工程, 2020, 28(3): 736. FENG Wei, WU Gui-ming, ZHAO Da-xing, LIU Hong-di. Multi images fusion Retinex for low light image enhancement[J]. Optics and Precision Engineering, 2020, 28(3): 736.

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