Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems Supplementary Material
نویسندگان
چکیده
The supplementary material contains the proof of Remark 3.1 as well as some additional information about the numerical experiments that contribute to the understanding of the main paper. We present detailed qualitative and quantitative evaluation results for each of our two (demosaicking and deconvolution) exemplary linear inverse image reconstruction problems. These results include parameter values obtained with our grid search, reconstruction PSNR values and images. Proof of Remark 3.1 For the sake of readability let us restate the remark and the four algorithms with the proximal operators of the regularization R replaced by an arbitrary continuous function G.
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