# Is a convolutional layer capable of converting, for example, a binary image into an RGBA image?

I am asking this question for a better understanding of the concept of channels in images.

I am aware that a convolutional layer generates feature maps from a given image. We can adjust the size of the output feature map by proper padding and regulating strides.

But I am not sure whether there exist kernels for a single convolution layer that are capable of changing an {RGBA, RGB, Grayscale, binary} image into (any) another {RGBA, RGB, Grayscale, binary} image?

For example, I have a binary image of a cat, is it capable to convert it into an RGBA image of a cat? If no, can it at least convert a binary cat image into an RGBA image?

I am asking only from a theoretical perspective.

• You say "I am not sure whether there exist kernels for a single convolution layer", but note that the kernels in a CNN are usually learned. Note also that you don't need CNNs, for example, to convert RGB images into grayscale/binary ones. There are algorithms to convert RGB images into grayscale images. I'm not fully sure how this question that you're asking is related to your confusion about channels.
– nbro
Jul 31 at 12:55
• I'm also not sure why you're so confused about this concept, as it's not really anything special (usually it just refers to the 3rd dimension of the image or feature map, i.e. would be a synonym for depth, although in the case of the images the depth has some meaning to us, as each slice, for example, in RGB images, corresponds to the values of the red, green and blue color, hence the name RGB)
– nbro
Jul 31 at 12:56
• Please check here. It has two arguments in_channels, out_channels. What is the purpose of them? Example shows 16 input channels and 33 output channels. I am aware about images with 1 channel, 3 channels and 4 channels. @nbro Jul 31 at 12:58
• In the input layer of the CNN, typically, you will have in_channels == 1 or in_channels == 3. However, in hidden layers of CNNs, you can have in_channels == K for $K > 1$, because this corresponds to the depth of the feature map that you produced in the previous convolutional layer, which corresponds to the number of kernels that you applied to the input of the previous layer (I'm assuming a 2d convolution).
– nbro
Jul 31 at 13:03
• Yes, I think it's just an example.
– nbro
Jul 31 at 13:10

• The number of channels in the input and output image is irrelevant, except that more channels means the network has more data to learn from, obviously. does it mean it is possible to change image with $k$ channels with an image of $n$ channels?where $k \ne n$? Jul 30 at 22:33
• If possible, please add in answer that an image of $k_1$ channels can be converted by a convolution layer in to an image of $k_2$ channels where $k_1 \ne k_2$. Jul 31 at 0:17