In order to solve the problem of poor fusion between the spots of deformation camouflage and the background,a small-spot deformation camouflage design algorithm based on background texture matching is proposed in this...In order to solve the problem of poor fusion between the spots of deformation camouflage and the background,a small-spot deformation camouflage design algorithm based on background texture matching is proposed in this research.The combination of spots and textures improved the fusion of the spot pattern and the background.An adversarial autoencoder convolutional network was designed to extract background texture features.The image adversarial loss was added and the reconstruction loss was improved to improve the clarity of the generated texture pattern and the generalization ability of the model.The digital camouflage was formed by obtaining the mean value of the square area and replacing the main color.At the same time,the spots in the square area with a side length of 2 s were subjected to simple linear iterative clustering to form irregular small-spot camouflage.A dataset with a scale of 1050 was established in the experiment.The training results of three different loss functions were investigated.The results showed that the proposed loss function could enhance the generalization of the model and improve the quality of the generated texture image.A variety of digital camouflages with main colors and irregular small-spot camouflage were generated,and their efficiency was tested.On the one hand,intuitive evaluation was given by personnel observing the camouflage pattern embedded in the background and its contour map calculated by the canny operator.On the other hand,objective comparison result was formed by calculating the 4 evaluation indexes between the camouflage pattern and the background.Both results showed that the generated pattern had a high degree of fusion with the background.This model could balance the relationship between the spot size,the number of main colors and the actual effect according to actual needs.展开更多
目的:探究30%超分子水杨酸联合小光斑点阵CO_(2)激光治疗轻中度寻常痤疮的有效性和安全性。方法:选取2022年11月-2023年6月就诊于吉林大学中日联谊医院30例面部轻、中度寻常痤疮患者,以患者面部矢状线为界,按照随机数表将患者的双侧面...目的:探究30%超分子水杨酸联合小光斑点阵CO_(2)激光治疗轻中度寻常痤疮的有效性和安全性。方法:选取2022年11月-2023年6月就诊于吉林大学中日联谊医院30例面部轻、中度寻常痤疮患者,以患者面部矢状线为界,按照随机数表将患者的双侧面部随机分为对照侧和观察侧。对照侧(n=30),给予30%超分子水杨酸治疗(每4周治疗1次);观察侧(n=30),给予小光斑点阵CO_(2)激光治疗后即刻30%超分子水杨酸治疗(每4周治疗1次)。通过拍照、皮肤检测仪、皮损计数和痤疮综合分级系统(The global acne grading system,GAGS)评分等评估治疗的有效性;通过皮损清除率判定临床疗效,同时观察不良反应以评估安全性。治疗结束后12周对患者进行随访,评估复发情况。结果:治疗12周后,患者观察侧的临床疗效优于对照侧(P<0.05);观察侧的皮损清除效果明显优于对照侧(P<0.001);观察侧的GAGS评分改善程度明显优于对照侧(P<0.001)。治疗期间两侧均未出现严重不良反应。结论:30%超分子水杨酸联合小光斑点阵CO_(2)激光疗法对清除轻中度痤疮皮损优于30%超分子水杨酸单一治疗,具有见效快、安全性高、操作简单、患者满意度高等优点,且不增加患者相关不良反应。展开更多
基金funded by Natural Science Foundation of Jiangsu Province,China,grant number is BK20180579。
文摘In order to solve the problem of poor fusion between the spots of deformation camouflage and the background,a small-spot deformation camouflage design algorithm based on background texture matching is proposed in this research.The combination of spots and textures improved the fusion of the spot pattern and the background.An adversarial autoencoder convolutional network was designed to extract background texture features.The image adversarial loss was added and the reconstruction loss was improved to improve the clarity of the generated texture pattern and the generalization ability of the model.The digital camouflage was formed by obtaining the mean value of the square area and replacing the main color.At the same time,the spots in the square area with a side length of 2 s were subjected to simple linear iterative clustering to form irregular small-spot camouflage.A dataset with a scale of 1050 was established in the experiment.The training results of three different loss functions were investigated.The results showed that the proposed loss function could enhance the generalization of the model and improve the quality of the generated texture image.A variety of digital camouflages with main colors and irregular small-spot camouflage were generated,and their efficiency was tested.On the one hand,intuitive evaluation was given by personnel observing the camouflage pattern embedded in the background and its contour map calculated by the canny operator.On the other hand,objective comparison result was formed by calculating the 4 evaluation indexes between the camouflage pattern and the background.Both results showed that the generated pattern had a high degree of fusion with the background.This model could balance the relationship between the spot size,the number of main colors and the actual effect according to actual needs.
文摘目的:探究30%超分子水杨酸联合小光斑点阵CO_(2)激光治疗轻中度寻常痤疮的有效性和安全性。方法:选取2022年11月-2023年6月就诊于吉林大学中日联谊医院30例面部轻、中度寻常痤疮患者,以患者面部矢状线为界,按照随机数表将患者的双侧面部随机分为对照侧和观察侧。对照侧(n=30),给予30%超分子水杨酸治疗(每4周治疗1次);观察侧(n=30),给予小光斑点阵CO_(2)激光治疗后即刻30%超分子水杨酸治疗(每4周治疗1次)。通过拍照、皮肤检测仪、皮损计数和痤疮综合分级系统(The global acne grading system,GAGS)评分等评估治疗的有效性;通过皮损清除率判定临床疗效,同时观察不良反应以评估安全性。治疗结束后12周对患者进行随访,评估复发情况。结果:治疗12周后,患者观察侧的临床疗效优于对照侧(P<0.05);观察侧的皮损清除效果明显优于对照侧(P<0.001);观察侧的GAGS评分改善程度明显优于对照侧(P<0.001)。治疗期间两侧均未出现严重不良反应。结论:30%超分子水杨酸联合小光斑点阵CO_(2)激光疗法对清除轻中度痤疮皮损优于30%超分子水杨酸单一治疗,具有见效快、安全性高、操作简单、患者满意度高等优点,且不增加患者相关不良反应。
