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In the Rgb2Raw branch,the feature map before Mosaic is HxWx3,and you use this code to generate final raw output:
HxWx3
def mosaic(images): shape = images.shape red = images[:, 0, 0::2, 0::2] green_red = images[:, 1, 0::2, 1::2] green_blue = images[:, 1, 1::2, 0::2] blue = images[:, 2, 1::2, 1::2] images = torch.stack((red, green_red, green_blue, blue), dim=1) # images = tf.reshape(images, (shape[0] // 2, shape[1] // 2, 4)) return images
this just use litter data of images to generate raw, I find some details in your paper in Section3.1,
images
the Bayer sampling function f_Bayer is applied
So why just output a feature map with shape HxWx1 to full use the data?
HxWx1
The text was updated successfully, but these errors were encountered:
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In the Rgb2Raw branch,the feature map before Mosaic is
HxWx3
,and you use this code to generate final raw output:this just use litter data of
images
to generate raw, I find some details in your paper in Section3.1,So why just output a feature map with shape
HxWx1
to full use the data?The text was updated successfully, but these errors were encountered: