CODEWITHSUNDEEP
imageimage-processingconvolution
Avinash
Avinash

Reputation: 133

Image processing using convolution

Well, I was trying out convolution on grey scale images, but then when I searched for convolution on rgb images, I couldn't find satisfactory explanation. How to apply convolution to rgb images?

Upvotes: 0

Views: 533

Answers (2)

Saeid Hosseinipoor
Saeid Hosseinipoor

Reputation: 1

You can look at this in two different ways: First, you may convert the color image into an intensity image with a normal vector. The most applicable one is (.299, .587, .114) which is the natural gray scale conversion. To get intensity you need to convert I = .299*R + .587*G + .114*B.

If you are designing your own convolutional network and intend to keep the color channels as inputs, just treat colored image as a 4D tensor with 3 channels. For example if you have a (h x w) image, the tensor size is (1 x h x w x 3) and you may use a filter of size (kh x kw x 3 x f) which kh and kw are your filter sizes and f is the required output features.

Upvotes: 0

Cris Luengo
Cris Luengo

Reputation: 60780

A linear combination of vectors can be computed by linearly combining corresponding vector elements:

a * [x1, y1, z1] + b * [x2, y2, z2] = [a*x1+b*x2, a*y1+b*y2 , a*z1+b*z2]

Because a convolution is a linear operation (i.e. you weight each pixel within a neighborhood and add up the results), it follows that you can apply a convolution to each of the RGB channels independently (e.g. using MATLAB syntax):

img = imread(...);
img(:,:,1) = conv2(img(:,:,1),kernel);
img(:,:,2) = conv2(img(:,:,2),kernel);
img(:,:,3) = conv2(img(:,:,3),kernel);

Upvotes: 2

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