Research on Low Illumination Image Enhancement Algorithm Based on Convolutional Neural Network

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Abstract

: Low illumination images have insufficient local and global light exposure, loss of structural and detail information, and are prone to generating a large amount of noise. The overall image is grayish or even completely dark, and people often cannot recognize the content of the image with the naked eye. Image enhancement technology aims to enhance image brightness, adjust image contrast, restore hidden details in the Fark, and enhance the utilization value of images through corresponding technical means.The traditional low illumination image enhancement methods mainly focus on Histogram equalization and Retinex methods. Based on the drawbacks of the traditional methods, this paper studies the low illumination image enhancement algorithm based on Convolutional neural network, builds a mathematical model, and lays the foundation for subsequent experimental research and application.
基于卷积神经网络的低照度图像增强算法研究
低照度图像局部和全局光照不足,结构和细节信息丢失,容易产生大量噪声。整体图像呈灰色甚至全暗,人们往往无法用肉眼识别图像的内容。图像增强技术旨在通过相应的技术手段,增强图像亮度,调整图像对比度,还原图像中隐藏的细节,提高图像的利用价值。传统的低照度图像增强方法主要集中在直方图均衡化和Retinex方法上。针对传统方法的不足,本文研究了基于卷积神经网络的低照度图像增强算法,建立了数学模型,为后续的实验研究和应用奠定了基础。
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