[英]Combine layer chrominance with image luminance
I have read this Colorization paper and it said: 我已经阅读了这张Colorization论文 ,上面写着:
The output layer of the colorization network consists of a convolutional layer with a Sigmoid transfer function that outputs the chrominance of the input grayscale image.
着色网络的输出层由具有Sigmoid传递函数的卷积层组成,该函数输出输入灰度图像的色度。
and in order to get the colored image they said: 为了获得彩色图像,他们说:
the computed chrominance is combined with the input intensity image to produce the resulting color image.
将计算出的色度与输入强度图像合并,以生成最终的彩色图像。
So I have implemented it and get the output layer with depth two, but how can I get the color image? 因此,我已经实现了它并获得了深度为2的输出层,但是如何获得彩色图像呢? How can I combine the greyscale image luminance values with the output layer of depth 2 (a*b colors) to get the final image?
如何将灰度图像的亮度值与深度为2的输出层(a * b颜色)结合起来以获得最终图像?
I use tensorflow and python. 我使用tensorflow和python。
好的,我尝试通过使用Skimage库实现图像色度值的张量并通过相同的方法将其与亮度组合来实现它。
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