Bernoulli-Gaussian (BG) model in Expectation-Maximization Bernoulli-Gaussian Approximate Message Passing (EM-BG-AMP) algorithm is constrained by its symmetry and restricted in the approximation of the actual signal prior distribution. Gaussian-Mixture (GM) model in Expectation-Maximization Gaussian-Mixture Approximate Message Passing (EM-GM-AMP) algorithm is a high-order model of BG model and has quite high complexity. In order to solve these problems, the Bernoulli-Asymmetric-Gaussian (BAG) model was proposed. Based on the new model, by further derivation, the Expectation-Maximization Bernoulli-Asymmetric-Gaussian Approximate Message Passing (EM-BAG-AMP) algorithm was obtained. The main idea of the proposed algorithm was based on the assumption that the input signal obeyed the BAG model. Then the proposed algorithm used Generalized Approximate Message Passing (GAMP) to reconstruct signal and update the model parameters in iteration. The experimental results show that, when processing different images, compared to EM-BG-AMP,the time and the Peak Signal-to-Noise Ratio (PSNR) values of EM-BAG-AMP are increased respectively by 1.2% and 0.1-0.5 dB, especially in processing images with simple texture and obvious color difference changing, the PSNR values are increased by 0.4-0.5 dB. EM-BAG-AMP is the expansion and extension of EM-BG-AMP and can better adapt to the actual signal.