{"title":"基于细胞振动能量模型和亮度差异的弱光图像增强技术","authors":"","doi":"10.1016/j.cviu.2024.104079","DOIUrl":null,"url":null,"abstract":"<div><p>Low-light image enhancement algorithms play a crucial role in revealing details obscured by darkness in images and substantially improving overall image quality. However, existing methods often suffer from issues like color or lightness distortion and possess limited scalability. In response to these challenges, we introduce a novel low-light image enhancement algorithm leveraging a cell vibration energy model and lightness difference. Initially, a new low-light image enhancement framework is proposed, building upon a comprehensive understanding and analysis of the cell vibration energy model and its statistical properties. Subsequently, to achieve pixel-level multi-lightness difference adjustment and exert control over the lightness level of each pixel independently, a lightness difference adjustment strategy is introduced utilizing Weibull distribution and linear mapping. Furthermore, to expand the adaptive range of the algorithm, we consider the disparities between HSV space and RGB space. Two enhanced image output modes are designed, accompanied by a thorough analysis and deduction of the relevant image layer mapping formulas. Finally, to enhance the reliability of experimental results, certain image faults in the SICE database are rectified using the feature matching method. Experimental results showcase the superiority of the proposed algorithm over twelve state-of-the-art algorithms. 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引用次数: 0
摘要
低照度图像增强算法在揭示图像中被黑暗遮挡的细节和大幅提高整体图像质量方面发挥着至关重要的作用。然而,现有方法往往存在色彩或亮度失真等问题,而且可扩展性有限。为了应对这些挑战,我们引入了一种利用细胞振动能量模型和亮度差异的新型弱光图像增强算法。首先,在全面了解和分析细胞振动能量模型及其统计特性的基础上,我们提出了一种新的弱光图像增强框架。随后,为了实现像素级多亮度差调整,并独立控制每个像素的亮度等级,引入了利用威布尔分布和线性映射的亮度差调整策略。此外,为了扩大算法的自适应范围,我们考虑了 HSV 空间和 RGB 空间之间的差异。设计了两种增强型图像输出模式,并对相关图像层映射公式进行了深入分析和推导。最后,为了提高实验结果的可靠性,使用特征匹配方法修正了 SICE 数据库中的某些图像缺陷。实验结果表明,所提出的算法优于十二种最先进的算法。本文的资源代码将在 https://github.com/leixiaozhou/CDEGmethod 上发布。
Low-light image enhancement based on cell vibration energy model and lightness difference
Low-light image enhancement algorithms play a crucial role in revealing details obscured by darkness in images and substantially improving overall image quality. However, existing methods often suffer from issues like color or lightness distortion and possess limited scalability. In response to these challenges, we introduce a novel low-light image enhancement algorithm leveraging a cell vibration energy model and lightness difference. Initially, a new low-light image enhancement framework is proposed, building upon a comprehensive understanding and analysis of the cell vibration energy model and its statistical properties. Subsequently, to achieve pixel-level multi-lightness difference adjustment and exert control over the lightness level of each pixel independently, a lightness difference adjustment strategy is introduced utilizing Weibull distribution and linear mapping. Furthermore, to expand the adaptive range of the algorithm, we consider the disparities between HSV space and RGB space. Two enhanced image output modes are designed, accompanied by a thorough analysis and deduction of the relevant image layer mapping formulas. Finally, to enhance the reliability of experimental results, certain image faults in the SICE database are rectified using the feature matching method. Experimental results showcase the superiority of the proposed algorithm over twelve state-of-the-art algorithms. The resource code of this article will be released at https://github.com/leixiaozhou/CDEGmethod.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems