Clarification on PSNR/SSIM Calculation and Dataset Use for DRCT-L Model on Set5

[Ar0Kim]

Hello,

Thank you for sharing your project. I have a question regarding the PSNR and SSIM results for the DRCT-L model. Specifically, for a dataset like Set5, are the PSNR and SSIM values reported in the paper calculated as the average over each super-resolved image's individual PSNR and SSIM scores?
When I run the test.py file using the DRCT-L_X4.pth model, the PSNR log only shows a single best score, as seen below.

Did you use a separate script to calculate the PSNR and SSIM for each image individually and then compute their average?

Additionally, I am trying to replicate the paper’s result for Set5 inference with the DRCT-L model, which reports a PSNR of 33.37 and an SSIM of 0.9090. I downloaded the dataset from the link provided (Validation data link, https://github.com/ChaofWang/Awesome-Super-Resolution/blob/master/dataset.md),

and for Set5, the folder structure includes images ending in “LR.png” and “HR.png”.

In this context, should the dataroot_lq point to the folder containing images ending with “LR.png” and dataroot_gt point to the folder containing images ending with “HR.png”? If not, please let me know which PNG files should be used as HR and LQ images.

To achieve the reported PSNR and SSIM values, could you please clarify which exact dataset was used and provide any details or code regarding the metric calculations? Currently, the best scores I obtain are lower than those presented in the paper.

Thank you very much for your assistance.