The lasso and elastic net linear regression models impose a double-exponential prior distribution on the model parameters to achieve regression shrinkage and variable selection, allowing the inference of robust models from large data sets.

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However,in light of4 stochastic optimization, we argue that our random permutation does not seem problematic. For the imperfect ground truth experiments, SURE requires knownσnoisy, but our eSURE requires knownσgt7 (otherwise, they are not working). In Tables 2, 3, bothσnoisy and σgt are described in the second and third rows,8 respectively. However,your comment is correct for practical sense, so we did train one deep neural network with12 varyingσgt [1 10]andσnoisy [10.1 55]for blind color image denoising and tested onimages with afixed13 noise level (just like Table 1) as shown in the below table. ToReviewer2It is indeed a good idea to be more explicit in some explanations for easier understanding.