The key issue in improving compressed sensor signal reconstruction accuracy is to design an effective perception matrix. Therefore, this paper presents a transfer learning-based perception matrix optimization method. First, migration learning is used to update the sparse representation coefficients and the fixed sparse basis is converted into a sparse adaptive basis. Then, the Gram matrix is constructed using the product of the sparse basis and measurement matrix. Finally, the nondiagonal elements of the Gram matrix are minimized by eigen decomposition to reduce global coherence of the Gram matrix and achieve accurate reconstruction of the original signal. The experimental results show that the method’s reconstructed image peak signal-to-noise ratio is higher and its complexity is lower compared to the other methods.