To preserve the image edge detail and avoid introducing false information during noise removal. This study proposes to detect noise point and improve image de-noising performance using the fractional differential gradient based on fractional calculus. The convolution of different-directions factional differential gradient template with noisy images is performed to calculate the fractional differential gradient in different directions. Images of the different directions fractional differential gradient are obtained according to a pre-set threshold value. The pixel is determined as a noise point when its gradient occurs along all selected directions. Only the detected noise points are processed by the variable-order fractional integration operator in eight directions. De-noising experiments that involve adding Gaussian noise or impulse noise in artificial and natural images arrive at the same conclusion. The visual effects serve as the subjective criteria

and the peak signal-to-noise ratios serve as the objective evaluation criteria. As the integral order v increases

the image de-noising effect increases

the image texture details become smooth

the image appears blurry

and the peak signal-to-noise ratio decreases. In addition

the de-noising based on the detected noise points and the efficiency of removed noise are enhanced with increasing integral order v. Noise detection based on the fractional differential gradient can help solve the contradiction between image de-noising and detail texture preservation. The proposed technique improves image noise detection accuracy. The results of this study may serve as a basis for improving the performance of the current de-noising algorithm.