The invention discloses a compressed learning perception based SAR (Synthetic Aperture Radar) image high-resolution reconstruction method which is mainly used for solving the problem that the quality of a reconstructed image is reduced because a sparse base and an observation matrix cannot meet restricted isometry property (RIP) existing in the conventional method. The method comprises the following steps of: inputting a training sample set and a test image; learning a dictionary and the observation matrix by using a Couple-KSVD (Kernel Singular Value Decomposition) method to obtain a target training dictionary psi and a coupled observation matrix phi; finally reconstructing a small block of a high-resolution image by using a fast Bayesian matching pursuit FBMP algorithm; and repeating the operation on all small blocks of the image to acquire a final SAR high-resolution reconstructed image. By adopting the method, the reconstruction quality of various SAR high-resolution images can beimproved at different sampling rates; and the method can be used for recovery and recognition of targets and objects in various SAR images.