计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 199-209.doi: 10.11896/jsjkx.210400092
赵征鹏1, 李俊钢1, 普园媛1,2
ZHAO Zheng-peng1, LI Jun-gang1, PU Yuan-yuan1,2
摘要： 利用传统Retinex模型进行低照度图像分解和增强时,需要人工不断地进行参数调试以达到最优解,这会降低整个过程的效率。此外,现有的基于Retinex理论的低照度图像增强方法在进行图像增强时未能很好地兼顾反射分量和光照分量,会存在低照度图反射分量噪点多、光照分量亮度低且细节不够突出的问题。基于此,提出了一种数据驱动的深层网络来学习低照度图像的分解和增强,通过端到端的网络训练来进行模型参数的学习。该网络先将低照度图分解为反射分量和光照分量,针对反射分量噪点多的问题,采用改进的去噪卷积神经网络(New Denoising Convolutional Neural Network,NDnCNN)模型进行去噪;针对光照分量亮度低、细节不够突出的问题,引入卷积块注意力模型(Convolutional Block Attention Model,CBAM)进行细节增强并指导网络进行光照分量的修正;最后用去噪后的反射分量和修正后的光照分量进行图像重建。经测试,增强后的低照度图亮度提升,细节突出,信息丰富,图像失真小且真实自然。
中图分类号:
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