Closed forseeking closed 5 years ago
Y channel for 4 conventional benchmark datasets (Set5, Set14, B100, Urban100), and RGB for DIV2K validation set. Sorry for the confusion.
Thanks a lot. So the input to the neural network is only the Y channel for the (Set5, Set14, B100, Urban100) test. Are they all trained with the DIV2K dataset?
I'm sorry, I just gave you the wrong answer.
Because our model only works with 3-channel inputs, all inputs and outputs have RGB channels. However, when evaluating the PSNR and SSIM, only Y channel was used for 4 benchmarks, and RGB channels were used for DIV2K valset.
All models were trained on DIV2K training set (800 images) using RGB channels.
Noted with thanks!
I have use RGB channels and Y channel to calculate the psnr, the psnr is 35.98 and 32 indenpendently. (DIV2K bicubic x2 validation 100 images). Higher to 35.03 in the paper
Does 800 images cause overfitting? It seems that 800 images are few。
We observed overfitting with EDSR, when we didn't use x8 geometric augmentation.
Hi, LimBee,Your work is really great! There is a question I would like to ask you: the model trains the images of the three channels of RGB, so the output picture is also three channels. My problem is that there is a picture in the Set14 "bridge" is a single-channel picture, so the test results of the Set14 provided by you include this picture? If the number of test pictures is 14, then what should I do? Thank you!
@qingchuanhuajuan Yes we included that picture, by concatenating the image 3 times to make 3-channel image.
What is the input channel in the Set5 / Set14 test in the paper? RGB or Y? Thanks for your answer.