应用于彩色图像数字化过程的图像校正技术研究

摘要

The digitization of images and documents has become an indispensable part of modern information construction.Aiming at the problem of image correction in the process of digitization of color art images,an image processing algorithm based on improved imaging model and deep neural network is proposed in this paper,which could effectively remove noise and increase image clarity.Firstly,an improved imaging model composed of global illumination,local illumination and reflectance is established,and the input RGB color image is converted into an HSV color image.Then extract the local features of the input image,and use these features to build a deep neural network to achieve noise removal.Simulation results show that compared with the traditional BP neural network and generalized regression neural network,the proposed algorithm has stronger image correction capability,higher peak signal-to-noise ratio and contrast increment.Therefore,the proposed algorithm is feasible and advanced.%图像和文档的数字化已经成为了现代信息化建设必不可少的内容.针对彩色美术图像数字化过程的图像校正问题,提出一种基于改进成像模型和深度神经网络的图像处理算法,能够有效去除噪声并增加图像清晰度.首先构建了由全局照度、局部照度和反射率组成的改进成像模型,并将输入RGB彩色图像被转换为HSV彩色图像.然后提取输入图像的局部特征,并利用这些特征构建一个深度神经网络以便实现噪声去除.仿真试验结果显示,相比传统BP神经网络和广义回归神经网络,提出算法的图像校正能力更强,具有较高的峰值信噪比和对比度增量,验证了提出算法的可行和先进性.