为了突出图像纹理细节的同时保留平滑区域，节省确定分数阶微分阶次的时间，提出了一种改进的自适应分数阶微分算子。首先将经典Tiansi 模板分解为四个不同方向，分别与待处理像素点进行卷积，达到增强图像纹理细节的效果；其次针对Tiansi 算子需通过多次实验确定最佳微分阶次的现状，结合图像的局部特征信息，构建了具有自适应能力的分数阶阶次模型，能够获得比原图像更丰富的细节信息。对多组不同场景图像的实验结果表明：构建的自适应分数阶微分算子有效地增强了图像的纹理细节，自适应分数阶微分算子的主观视觉效果和客观评价指标均优于原图像，其客观评价指标中的平均梯度、信息熵、对比度相比原图像平均提高190.3%、8.1%、18.3%；平均梯度、对比度相比Tiansi 算子处理后的图像平均提高45.0%、9.6%。

In order to highlight the texture details of the image while preserving the smooth region and saving the time to determine the fractional differential order, an improved adaptive fractional differential operator is proposed. Firstly, the classical Tiansi template is decomposed into four different directions, which are respectively convolved with the pixels to be processed to achieve the effect of enhancing the texture details of the image. Secondly, the current situation of the optimal differential order is determined by the experiment for the Tiansi operator. The local feature information of the image constructs a fractional order model with an adaptive ability, which can obtain more detailed information than the original image. The experimental results of multiple sets of different scene images show that the constructed adaptive fractional differential operators effectively enhance the texture details of the image. The subjective visual effects and objective evaluation indexes of the adaptive fractional differential operators are better than the original images. The average gradient, information entropy and contrast in the objective evaluation index are increased by 190.3%, 8.1%, and 18.3%, respectively. The average gradient and contrast are 45.0% and 9.6% higher than that of the Tiansi operator.