首页|Reports Outline Intelligent Systems Study Results from Zhengzhou Normal Universi ty (Traditional landscape painting and art image restoration methods based on st ructural information guidance)
Reports Outline Intelligent Systems Study Results from Zhengzhou Normal Universi ty (Traditional landscape painting and art image restoration methods based on st ructural information guidance)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on intelligent systems are presented in a new report. According to news reporting out of Zhengzhou, People’ s Republic of China, by NewsRx editors, research stated, “In the field of tradit ional landscape painting and art image restoration, traditional restoration meth ods have gradually revealed limitations with the development of society and tech nological progress.” Our news journalists obtained a quote from the research from Zhengzhou Normal Un iversity: “In order to enhance the restoration effects of Chinese landscape pain tings, an innovative image restoration algorithm is designed in this research, c ombining edge restoration with generative adversarial networks (GANs). Simultane ously, a novel image restoration model with embedded multi-scale attention dilat ed convolution is proposed to enhance the modeling capability for details and te xtures in landscape paintings. To better preserve the structural features of art istic images, a structural information-guided art image restoration model is int roduced. The introduction of adversarial networks into the repair model can impr ove the repair effect. The art image repair model adds a multi-scale attention m echanism to handle more complex works of art. The research results show that the image detection model improves by 0.20, 0.07, and 0.06 in the Spearman rank cor relation coefficient, Pearson correlation coefficient, and peak signal-to-noise ratio (PSNR), respectively, compared to other models.”
Zhengzhou Normal UniversityZhengzhouPeople’s Republic of ChinaAsiaIntelligent SystemsMachine Learning
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