一个深度代照明估计网络用于低光图像增强,基于视网膜理论
Yongqiang Chen1, Chenglin Wen2,3, Weifeng Liu4
1School of Automation, Guangdong University of Petrochemical Technology, Maoming, 525000, China.
Scientific reports
|November 12, 2023
概括
这项研究介绍了一种新的照明增强网络,使用Retinex理论来改善低光图像. 该方法有效地提高了亮度,抑制了噪音,并保留了细节,优于现有的技术.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 低光图像增强在视觉质量,计算效率,消除噪音和照明调整方面面临挑战.
- 现有的方法与极其黑暗的场景作斗争,需要先进的解决方案.
研究的目的:
- 为低光图像提出基于Retinex理论的快速准确的照明增强网络.
- 解决现有技术在视觉质量,噪音和照明调整方面的局限性.
主要方法:
- 开发了一个基于学习的双阶段网络:分解网络和增强网络.
- 分解网络将图像分成反射率和照度图.
- 增强网络使用增强照明和反射模块,使用级联代学习和重量共享来准确估计.
主要成果:
- 拟议的框架有效地抑制噪音,并保留低光图像中的细节.
- 在LOL数据集上实现了高峰信号噪声比率 (PSNR) 比Retinex-Net增加9.16%.
- 与最先进的SCI方法相比,显示了19.26%的改善.
结论:
- 新型照明增强网络在低光图像处理中提供了卓越的性能.
- 该方法在亮度,噪音降低和细节保存方面提供了显著的改进.
- 无监督的训练损失增强了模型对各种低光条件的概括能力.
相关概念视频
01:24Vision
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
02:16Light Acquisition
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.