To overcome the edge blurring and the difficulty in edge detection of noisy images, a method for edge detection of noisy images in nonsubsampled contourlet transform (NSCT) domain is proposed based on fractional differentiation. This method first decomposes the image by NSCT, and then extracts the contours of the low-frequency sub-bands. Second, for the high-frequency sub-bands in various directions with more edge details and noise, the proposed method uses the multi-scale product of the NSCT domain and the direction fractional differential matrix to perform threshold denoising and enhance information on high-frequency coefficients. Finally, the scale images of each frequency domain and direction in the NSCT domain are fused to obtain a complete edge image. Experiments are carried out on different types of original and noisy images, and the average continuous edge pixel ratio obtained by the proposed method is 0.931 and 0.861, respectively. Compared with Canny operator, fractional differential detection method, and existing multiscale domain edge detection methods, this method has better edge detection effect. With the increase of the image noise level, we can obtain a high average continuous edge pixel ratio, strong anti-noise, and accurate, complete and continuous edges by the proposed method.