基于深度学习的区域背光亮度提取方法  

Regional Backlight Brightness Extraction Algorithm Based on Deep Learning

作　　者：张涛 曾琴[1,2] 杜文丽 王昊 Tao Zhang;Qin Zeng;Wenli Du;Hao Wang(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China;Texas Instruments DSP Joint Lab,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津大学电气自动化与信息工程学院,天津300072 [2]天津大学德州仪器DSP联合实验室,天津300072

出　　处：《光学学报》2020年第22期73-81,共9页Acta Optica Sinica

基　　金：国家自然科学基金(61350009,61179045);华为创新研究计划(HO2018085418)。

摘　　要：在分析现有算法的优缺点的基础上,尽可能考虑各种类型的图像,基于发光二极管-液晶显示器直下式图像视频显示原理样机,提出了一种可靠有效的基于区域背光亮度提取的数据测量方法,并提出了一种高效实用的基于深度学习的背光亮度提取方法。该方法基于采用多层下采样结构的神经网络,综合提取图像特征,获得最优的背光。实验结果表明,该方法可以提高图像显示质量和扩大图像的动态范围,而网络结构中有无旁路的对比实验结果验证了所提方法的优越性和有效性。Based on the analysis of the advantages and disadvantages of the existing algorithms,various types of images are considered as far as possible.Based on the principle prototype of light emitting diode-liquid crystal display direct-down image and video display,a reliable and effective data measurement method based on regional backlight brightness extraction is proposed,and an efficient and practical backlight brightness extraction method based on deep learning is proposed.The method is based on the neural network with a multi-layer down sampling structure to extract the image features and obtain the optimal backlight.The experimental results show that the method can improve the image display quality and expand the dynamic range of images.The experimental results of network structures with or without bypass verify the superiority and effectiveness of the proposed method.

关 键 词：图像处理 液晶显示 区域调光 神经网络 背光提取 

分 类 号：O436[机械工程—光学工程]