Traditional phase retrieval algorithms only utilize prior information, such as the non-negative constraints and support constraints of the signal, which makes it difficult to effectively reconstruct original signal undersampling conditions. Under the theoretical framework of compressive sensing, combined with the sparsity of natural images in the gradient domain, fractional total variation is incorporated into the phase retrieval model as prior information, and the proposed nonconvex optimized phase retrieval model is solved using the alternating direction multiplier method. The experimental results indicate that fractional order total variation converges faster than does integer order total variation at lower sampling rates. Compared with classical phase retrieval algorithms, such as HIO and RAAR, the proposed algorithm has stronger detail reconstruction ability in amplitude information phase retrieval with a low sampling rate and Gaussian noise pollution.