首页|利用自适应光照初始化的弱光图像增强方法

利用自适应光照初始化的弱光图像增强方法

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由于光照分量分解估计的高度不确定性,如何准确估计图像的光照分量一直是基于Retinex模型的图像增强方法需要解决的难题.该文提出一个简单有效的方法,准确估计图像的初始光照分量,进而实现弱光图像增强.具体地,首先根据输入图像得到其对应的光照权重矩阵,以指导光照分量的自适应初始化估计;随后在光照结构约束下,对初始光照分量优化估计,并进一步执行非线性光照调整;最终结合Retinex模型得到增强结果.实验表明,该方法不仅能够实现准确的图像分解估计,而且与现有的弱光图像增强方法相比,该文所提方法在多个数据集上的主观视觉效果和客观评价指标都有更好的表现,同时也保持着良好的运行效率.
Low Light Image Enhancement With Adaptive Light Initialization
Due to the high uncertainty in the estimation of the light component decomposition, how to accurately estimate the light component of an image has been a challenge to be addressed by image enhancement methods based on the Retinex model. An effective method is proposed to accurately estimate the initial illumination component in this paper. Specifically, the corresponding illumination weight matrices for different inputs are obtained to guide the adaptive initialization estimation, subsequently the estimation of the initial illumination components are optimized under the constraints of the illumination structure, and the non-linear illumination adjustment be performed on them. Finally, the Retinex be combined to obtain the enhanced images. Experiments show that our method not only achieves accurate image decomposition estimation, but also performs better in terms of both subjective visual effects and objective evaluation metrics on multiple datasets while maintaining good operational efficiency compared with existing methods for low-light image enhancement.

Low-light image enhancementRetinexAdaptive estimation of illumination

刘波、田广粮、肖斌、马建峰、毕秀丽

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重庆邮电大学图像认知重庆市重点实验室 重庆 400065

西安电子科技大学网络与信息安全学院 西安 710071

弱光图像增强 Retinex模型 光照自适应估计

重庆自然科学基金杰出青年科学基金国家自然科学基金国家自然科学基金重庆市教委科学技术研究计划

CSTB2022NSCQ-JQX00016217206761976031KJQN202200635

2024

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子:1.302
ISSN:1009-5896
年,卷(期):2024.46(2)
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