This paper proposes a digital image restoration algorithm for phase-coded imaging systems. In order to extend the depth-of-field (Dof), an imaging system equipped with a properly designed phase-coded lens can achieve an approximately constant point spread function (PSF) for a wide range of depths. In general, a phase-coded imaging system produces blurred intermediate images and requires subsequent restoration processing to generate clear images. For low-computational consumer applications, the kernel size of the restoration filter is a major concern. To fit for practical applications, a pyramid-based restoration algorithm is proposed in which we decompose the intermediate image into the form of Laplacian pyramid and perform restoration over each level individually. This approach provides the flexibility in filter design to maintain manufacturing specification. On the other hand, image noise may seriously degrade the performance of the restored images. To deal with this problem, we propose a Pyramid-Based Adaptive Restoration (PBAR) method, which restores the intermediate image with an adaptive noise suppression module to improve the performance of the phase-coded imaging system for Dof extension.