亮度——梯度联合约束的车牌图像超分辨率重建  

License plate image super-resolution based on intensity-gradient prior combination

作　　者：孙京 袁强强[3] 李冀玮 周春平 沈焕锋[1,2] Sun Jing;Yuan Qiangqiang;Li Jiwei;Zhou Chunping;Shen Huanfeng(Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China;School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China;School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;Beijing Remote Sensing Information Institute, Beijing 100011, China)

机构地区：[1]首都师范大学北京成像技术高精尖创新中心,北京100048 [2]武汉大学资源与环境科学学院,武汉430079 [3]武汉大学测绘学院,武汉430079 [4]北京遥感信息研究所,北京100011

出　　处：《中国图象图形学报》2018年第6期802-813,共12页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61671334;41661134015)~~

摘　　要：目的受成像距离、光照条件、动态模糊等因素影响,监控系统拍摄的车牌图像往往并不具备较高的可辨识度。为改善成像质量,提升对车牌的识别能力,提出一种基于亮度与梯度联合约束的车牌图像超分辨率重建方法。方法首先充分结合亮度约束和梯度约束的优势,实现对运动位移和模糊函数的精确估计;为抑制重建图像中的噪声与伪影,基于车牌图像的文字化特征,进一步确定了亮度与梯度联合约束的图像先验模型。结果为验证该方法的有效性,利用监控系统获得4组车牌图像,分别进行模拟和真实的超分辨率重建实验。在模拟实验中将联合约束图像先验重建结果与拉普拉斯、Huber-Markov(HMRF)以及总变分(TV)先验的处理结果进行对比,联合约束先验对车牌纹理信息的恢复效果优于其他3种常见图像先验;同时,在模拟和真实实验中,将本文算法与双三次插值、传统最大后验概率、非线性扩散正则化和自适应范数正则化方法的超分辨率重建结果进行比较,模拟实验的结果表明,在不添加噪声情况下,该算法峰值信噪比(PSNR)和结构相似性(SSIM)指标分别为35.326 d B和0.958,优于其他4种算法;该算法在真实实验中,能够有效增强车牌图像纹理信息,获得较优的视觉效果,通过对重建车牌图像的字符识别精度比较,本文算法重建结果的识别精度远高于其他3种算法,平均字符差距为1.3。结论模拟和真实图像序列的实验结果证明,基于亮度—梯度联合约束的超分辨率重建方法,能够降低运动和模糊等参数的估计误差,有效减少图像中存在的模糊和噪声,提高车牌的识别精度。该算法广泛适用于因光照变化、相对运动等因素影响下的低质量车牌图像超分辨率重建。Objective License plate images captured by monitoring systems often have relatively low spatial resolution andare thus difficult to identify due to the large distances between vehicles and cameras,the low resolution of the imaging devices used for video images,and factors such as atmospheric disturbances,lighting conditions,and motion blur. The highresolution reconstruction of low-resolution license plate images is crucial in enhancing license plate image resolution and thus increasing the accuracy of license plate recognition. Multiframe super-resolution techniques are particularly well suited for this application because they facilitate recovery of valid results from low-quality images by gathering information not only from the image itself but also from the constraints that must be satisfied. In this work,a super-resolution reconstruction algorithm based on intensity-gradient prior combinations is proposed to improve the quality and detectability of license plate images. Method The proposed algorithm includes three steps. First,the motion displacement between the multiframe images is estimated with a novel optical flow estimation method under a robust data function. The data fidelity model that adds gradient constancy constraints to the color constancy constraint is more accurate in terms of modeling the confidence of pixel correspondence than that using only one out of the two terms,which is not appropriate because the color constancy constraint is often violated when illumination or exposure changes. In the optical flow estimation model,the selective combination of the color and gradient constraints in defining the data term enables the recovery of many motion details that are robust to outliers. The blur function of the reference license plate image is then estimated with a blind deblurring method that is based on regularized intensity and gradient prior in the second step of the algorithm. The proposed intensity and gradient prior,which is based on distinctive properties such that text and background regio

关 键 词：超分辨率重建 联合约束 最大后验概率 光流估计 模糊估计 图像先验 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]