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I would like to ask the author, if the size of the image used for network training is 960x540, then when I want to use this network test, the resolution of the photos taken by my camera is very large, such as 4608x3456. When I take this picture to test, the parallax map effect is very bad. When I resize the original image to 960x540, the parallax map effect is very good OK, but I don't know how to restore the parallax value of the original image. If the high-resolution image(4608x3456) is cut(960x540) and then put into the prediction, the prediction effect of each small resolution image cut out is not good.Or how to use your network to test higher resolution images without enough similar data sets to train? Hope that the author can guide, thank you very much!
The text was updated successfully, but these errors were encountered:
same here. I have a high-resolution image. How can I use GANET pre-trained models? resizing the input image will degrade its resolution. For example, HDR resolution is 1920*1080.
I would like to ask the author, if the size of the image used for network training is 960x540, then when I want to use this network test, the resolution of the photos taken by my camera is very large, such as 4608x3456. When I take this picture to test, the parallax map effect is very bad. When I resize the original image to 960x540, the parallax map effect is very good OK, but I don't know how to restore the parallax value of the original image. If the high-resolution image(4608x3456) is cut(960x540) and then put into the prediction, the prediction effect of each small resolution image cut out is not good.Or how to use your network to test higher resolution images without enough similar data sets to train? Hope that the author can guide, thank you very much!
The text was updated successfully, but these errors were encountered: