摘要
暗光环境下成像往往受到低照度和成像噪声等多种因素干扰,所得图片的视觉质量往往较低。当前各类暗光增强方法多侧重于改善可视度,却常忽略了保持增强结果真实感这一同样重要的目标。为解决该问题,提出了一种自适应去噪保真的无监督暗光图像增强方法,旨在高效便捷地实现改善图像可视度和去噪保真两个目标。模型由暗光增强阶段和去噪保真阶段组成。在暗光增强阶段,构建无监督图像分解模块和光照增强模块,实现改善可视度的目标;在去噪保真阶段,基于前一阶段所得的光照分布来自动构造成对训练数据,驱动去噪模块抑制原本昏暗处的噪声并保持原本明亮处的细节,实现增强结果保真的目标。实验结果表明,相比其他暗光增强方法,所提方法在改善可视度和去噪保真之间能够取得良好的均衡。该模型无须事先采集或准备“昏暗-明亮”成对图像来进行训练,且具有较小的模型尺寸和较快的计算速度,实用性良好。
The visual quality of images taken under low-light environment is usually low,due to many factors such as low lightness and imaging noise.Current low-light image enhancement methods have a common limitation that they only focus on improving lightness condition and suppressing noise,but neglect to preserve image details.To solve this problem,an unsupervised low light image enhancement method is proposed in this paper,aiming to improve the visibility and preserve the fidelity of an image with good efficiency.The model consists of two stages,i.e.,low-light enhancement and noise suppression.In the first stage,an unsupervised image decomposition module and a lightness enhancement module are constructed to achieve the goal of improving visibility.In the second stage,under the guidance of the illumination distribution of an image,we synthesize pairwise training data and train the denoising network to depress the imaging noise from the originally-dim regions and preserve the image details of the originally-bright regions.Compared with other methods,experimental results show that our method achieves better balance between the goals of visibility improvement and fidelity preservation.In addition,our method can be attractive in real-world applications,as it does not need to collect bright-dim image pairs,and it has small model size and fast calculation speed.
作者
高仁
郝世杰
郭艳蓉
GAO Ren;HAO Shijie;GUO Yanrong(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China)
出处
《计算机科学》
CSCD
北大核心
2024年第3期147-154,共8页
Computer Science
基金
国家自然科学基金(62172137)。
关键词
暗光图像
光照增强
噪声抑制
图像分解
Low-light image
Lightness enhancement
Noise suppression
Image decomposition
