Aashish Sharma

Hi @alex04072000, Thanks for publishing your code and datasets. Will it be possible for you to also release the training scripts/code? Best!

Hi @KeqiWangSXuniversity, This is from the author's code itself. Assuming ``raw`` is the RAW data, and ``rgb`` is the processed RGB data you'd like to have, you can do -...

Hi @noamgot, Could you please tell me how you did that? I too have some NEF files I would like to test this code on. Thanks in advance!

Hi @noamgot Many thanks for your reply. I've a NIkon D80 camera, and when I check the black level using the same snippet above, I get 0, and the max....

Dear @cchen156 @noamgot Also, I've got some preliminary results by doing 1) Normalizing by ``im = np.maximum(im - raw.black_level_per_channel[0], 0) / (16383 - raw.black_level_per_channel[0]) `` 2) Modifying the packing operation...

Dear @noamgot, Yes, the results are OK, but I'm unsure whether they are optimal, or there is still some problem in my test code. Also, thanks again for all your...

Hi @troyliuyue , This is what I observe for my data (captured using Nikon D80) -  This is what I observe for the available RAW files from the Sony...

Hi @KashyapCKotak , As per my understanding, the pattern [[0, 1]; [3, 2]] denotes [[R, G]; [G, B]] pattern , ({0,1,2} for {R,G,B} and 3 for the extra G channel)....

Nothing specific with 512, but we need to ensure that the patch size is a power of 2, so there is no ambiguity during downsampling within the network (imagine if...

The basic idea is to normalize the RAW values from their [min, max] to [0, 1]. The min. would be decided by the black level of the camera. You can...