Metrics on RGB or Y?

[jiaqixuac]

Hi,

Thank you for your nice work.
I want to know how you calculate the metrics for reporting.

Here are some results using your code and your model weights.

1. python inference.py --checkpoint student-setting1.pth xxx and save the restored images.
2. Calculate psnr and ssim using
   a) RGB: torchPSNR(pred_image, gt_image) + pytorch_ssim.ssim(pred_image, gt_image);
   b) Y: torchPSNR(rgb2ycbcr(pred_image[0]), rgb2ycbcr(gt_image[0])) + sk_cpt_ssim(rgb2ycbcr(pred_image[0]), rgb2ycbcr(gt_image[0]), data_range=1.0, multichannel=True).

The val data (and numbers) are: SOTS outdoor (500), Rain1400 (1400), CSD (2000).

Here are my results compared to the CVPR version:

	Haze	Rain	Snow
RGB	31.35/0.9441	30.54/0.9004	30.10/0.9334
Y	33.39/0.9693	32.51/0.9218	31.68/0.9495
Yours	33.95/0.98	33.13/0.93	31.35/0.95

I think the metrics calculated on the Y channel are close to yours.
Can you clarify this? Thanks!

[huang-hz]

I get the same value on Rain1400 testset

[jjb202]

Do you generate the meta file for training? can you provide the generate code by dataset path?