sjmoran / deeplpf Goto Github PK

Hello, thank you for excellent work! I have met some problem when I trained and tested your model on the dataset mentioned in your paper. I follow the ways mentioned in DPE to process the MIT Adobe FiveK dataset and use their test list for testing. However, I got very poor performance with PSNR 17.49 dB on the FiveK-DPE test dataset.
the following is my processed image paired.

input image

expertC image

Hey **Sean, totally am inspired by your work.

I've turned DeepLPF into device-agnostic code, to run on "cpu" (I'm on M1 mac and "mps" is still unreliable). I've been successful in testing images based on your existing checkpoints, however am getting strange results when training on my own data, and I can't figure out what would be giving this "look". I've followed the training data image prep as per your readme file.
input:

groundtruth:

test:

Any suggestions?

Hi,
I believe the paper and poster both show a loss function with contradictive parts: the first part (Lab) gets bigger as images are more different, whereas the second part (MS-SSIM) gets bigger as images are more similar.

The code, however, does not make that mistake, and actually the loss is Lab + (1 - MS-SSIM) (not sure why 1 is necessary).

Line 232 in data.py should be img_id = file.split(".")[0] instead of img_id = file.split("-")[0]

Hello,
Could you please explain how DeepLPF handles various image sizes at (1) train time (2) inference/prediction time?
Also if you could point to parts of the code that support that, that would be highly appreciated.
Thanks!

I want to train the DeepLPF again, but I see the data.py only have 248 lines, so where should I modify the path?

In the other hand, I want to know what means of 3 pre-trained models, I think the adobe_dpe is 2250 training images and 500 testing images, adobe_upe is 4500 training images and 500 testing image, but how about distort-and-recover?

Last question, how do you export the MIT-ADOBE-FIVEK datasets, do you have do any operation such as resizeing?

I use fivek datasets last 500 images as testing images which resize to have a long-edge of 512 pixels in your three pre-trained model and get a not very good result (psnr 21.57 in dpe, 22.51 in upe and 20.87 in distort-and-recover)

Hi,
According to your poster, the elliptical and graduated filters should be taking concat(y_hat_2, backbone_features) as input, yet according to the following code (see starting point), all 3 filter block types (incl. polynomial/cubic) take concat(y_hat_1, backbone_features)

Could you please help clarify?

Thanks!

Hello, Authors! Thanks for your excellent work. I would like to know whether it is possible to test custom images other than the Adobe dataset. In the code, I found that the data loader is just for Adobe.

Deer author,I want to know when you will update higher batch sizes。

The original mit-adobe-fivek dpe if too big for me and as you said"Lightroom to pre-process the images according to the procedure outlined in the DeepPhotoEnhancer (DPE) paper". Can you provide with a processed input dataset?

RuntimeError: Given groups=1, weight of size [16, 3, 3, 3], expected input[1, 4, 345, 514] to have 3 channels, but got 4 channels instead

Thank you for your amazing work.
But I'm sorry that the link in the readme is broken:
https://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/%5BOnline_Demo_Models%5D_Deep-Photo-Enhancer.zip

So could you please release the training, validation and testing dataset splits for Adobe-DPE

Hello, I'm very interested in your great work. Could you upload your pre-trained models? Thanks.

Adobe-UPE dataset can't bt found, could you provide another link please?