The MAP(maximizing a posteriori) estimation can be use to reconstruct degraded images and effectively reduce the artifacts of the restored images; but the MAP estimation is non-zero gradient of penalty images, and the image priori estimation ignores the own texture of the degraded images, resulting in overly smooth restored images, with abrupt step edges and a loss of mid-frequency texture information. Therefore, the image reconstruction algorithm based on MAP estimator coupled iteration distribution reweighting was proposed. The image conference gradient distribution calculation model was constructed based on the pixel of degraded images, to estimate the image priori. The KL divergence was introduced, in combination with MAP estimation, to penalize the gradients between the empirical and reference distributions. And iterative distribution reweighting algorithm was designed to minimize the cost function and optimize the empirical gradient distribution for improving the convergence precision, and making the empirical gradient distribution closer to reference distributions. Transduction contrast distortion model was constructed based on Human Visual System. Finally, user response study was conducted for the proposed algorithm using Amazon Mechanical Turk. The reconstruction quality of the proposed algorithm was high, and the user response was good.