Deep Wiener Wiener Meets Deep Learning for Image

In this supplemental material, we first present the detailed network architecture and parameters of the proposed approach in Sec. A. We further provide more analysis of the proposed method and ablation studies in Sec. B. Section C shows some qualitative results for potential applications of the proposed approach on medical imaging and imaging in astronomy. Our goal of this section is to complement the results in the main paper (in particular Tab. 3 in the The basic reconstruction network that contains three residual blocks followed by one convolutional layer is denoted as Basic reconstruction . We thus disable our proposed feature refinement network for all the baseline methods in this section. The results in Tab. 8 demonstrate that the deep features are more effective for extracting useful The last row in Tab. 8 reports the results of the proposed approach with the deep Wiener deconvolution B.2 Effectiveness of learned deep features with a basic reconstruction network In the main paper, we compare the proposed method with state-of-the-art methods on the dataset of Levin et al. Figure 6: Illustration of learned deep features.