以数据为驱动构建数字校园生态体系的探索与实践——以柳州铁道职业技术学院为例

国家全面推动职业教育办学模式、教育教学形式、管理服务方式和人才培养的数字化转型,对数字校园建设提出了更多新要求。该文分析了在教育数字化转型背景下,如何以数据为驱动实施数字化工程建设,构建数字校园新生态,促进多元融合,化解“数据孤岛”难题,提升综合服务管理质量。以数据为驱动构建数字校园生态体系能够实现校园智慧高效的常态化管理,成为数字化赋能职业教育创新发展的典范。

结合自适应亮度变换不等式估计透射率的图像去雾方法

为了解决单幅图像去雾算法中透射率估计不准确和去雾不彻底问题,提出了一种结合自适应亮度变换不等式估计透射率的图像去雾方法。首先,利用亮度和反饱和度进行亮度变换不等式补偿构造场景深度,并使用对数变换进行灰度扩展;其次,采用正则化对场景深度优化,解决相邻像素的差异过大问题,逼近精确场景深度;然后,根据局部恒定假设,利用局部平均深度和联合双边滤波估计动态大气散射系数,结合场景深度和动态大气散射系数获得透射率;最后,使用中值滤波和区间估值优化局部大气光,并通过大气散射模型恢复无雾图像。实验结果表明:所提出的图像去雾方法可在不同场景自适应地调节模型参数,去雾彻底,获得了较好的去雾效果;同时,客观指标中平均视觉对比度达到了60.572,具有较好的图像保真度。

Dehazing algorithm using adaptive dark channel fusion and sky compensation

Aiming at the inaccurate transmission estimation problem of dark channel prior image dehazing algorithm in the sudden change area of depth of field and sky area,a dehazing algorithm using adaptive dark channel fusion and sky compensation is proposed.Firstly,according to the characteristics of minimum filtering of large window scale and small window scale in the dark channel prior,the fused dark channel is obtained by weighted fusion of the approximate depth of field relationship,thus obtaining the primary transmission.Secondly,use the down-sampling to optimize the primary transmission combined with gray scale image of haze image by fast joint bilateral filtering,then restore the original image size by up-sampling,and the compensation of the Gaussian function is used in the sky area to obtain corrected transmission.Finally,the improved atmospheric light is combined with atmospheric scattering model to recover haze-free image.Experimental results show that the algorithm can recover a large amount of detailed information of the image,obtain high visibility,and effectively eliminate the halo effect.At the same time,it has a better recovery effect on bright areas such as the sky area.