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作 者:燕雨 谭艳君 苗润丰[1,2] 高鹏福 Yan Yu;Tan Yanjun;Miao Runfeng;Gao Pengfu(College of Textile Science and Engineering,Xi’an Polytechnic University,Xi’an,Shaanxi 710048,China;Shaanxi Dyeing and Finishing Innovation Center,Xi’an Polytechnic University,Xi’an,Shaanxi 710048,China)
机构地区:[1]西安工程大学纺织科学与工程学院,陕西西安710048 [2]西安工程大学陕西省染整创新中心,陕西西安710048
出 处:《针织工业》2024年第1期52-57,共6页Knitting Industries
基 金:陕西省功能材料染整创新工程中心产学研项目(陕工信发[2020]341号)。
摘 要:织物色差等级评定目前主要依赖于人工对比标准样卡进行判定,其等级判断易受人为主观因素影响。为对棉织物色差进行客观、全局化评定,文中提出3种基于OpenCV的织物色差评定方法:首先在D_(65)光源下获取标准样卡的不同色差等级灰度图像,使用高斯滤波对图像进行降噪,然后分别采用MSE均方误差、PSNR峰值信噪比、SSIM结构相似性指数算法对标准样卡灰度图像进行量化处理,建立并寻找最佳模型。为验证模型的准确性,采用数码印花对经过不同前处理工艺的棉织物喷印染色作为测试样本,通过模型得到色差等级,并与人工评定色差结果对比。结果表明:3种图像处理评定织物色差模型的评定结果均与人工评定具有高度一致性,图像处理方法更加简洁、客观和高效。Fabric color difference rating mainly relies on manual comparison of standard sample cards for determination,and its rating is easily affected by human subjective factors.In order to carry out objective and global evaluation of cotton fabric's color difference,it proposed three OpenCV-based fabric color difference evaluation methods.Firstly,we obtained different color difference grayscale images of the standard sample card under D_(65) light source,used Gaussian filter to reduce the noise of the image,and then quantified the grayscale image of the standard sample card using MSE mean square error,PSNR peak signal-to-noise ratio and SSIM structural similarity index algorithm to establish and find the best model.In order to verify the accuracy,digital printing was used to spray dye cotton fabrics after different pretreatment processes as a test sample,and the color difference level was obtained through the model and compared with the manual evaluation of the color difference results.The results show that the evaluation results of the three kinds of image processing models for evaluating the color difference of fabrics are all in high consistency with the manual evaluation,and the image processing methods are more concise,objective and efficient.
分 类 号:TS197[轻工技术与工程—纺织化学与染整工程]
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