Review for NeurIPS paper: LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond

This submission proposes to do single image super-resolution using a network which produces coefficients for a fixed bank of Gaussian/DoG filters. The super-resolution results produce nearly SotA super-resolution PSNR while the proposed approach is 1-2 orders of magnitude more efficient than SotA. Reviewers liked the idea of incorporating a filter bank dictionary. While all of the reviewers felt that these weaknesses put the submission below the acceptance threshold, metareviewers felt that the authors' response adequately addressed each of these concerns. Please add comparisons with the SotA approaches (EDSR, RCAN, ESRGAN, ProSR) in terms of PSNR, efficiency (MultAdds), and parameter count.