How to train CNN with an image in the input and an image in the output?

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- 2021-05-17T15:29:23+00:00
Question: How to train CNN with an image in the input and an image in the output?

I have a task to train CNN with an image as input and an image as output. I have tried to do it at the beginning with Matlab tutorial, but matlab has no image as output, but a vector. https://de.mathworks.com/help/deeplearning/examples/train-a-convolutional-neural-network-for-regression.html any idea how i can do this tutorial again but with image as output? For example you could insert input as image for number one and the output is also for number one but rotated or deformed.    

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    • Expert Answer

    Profile picture of Kshitij Singh Kshitij Singh answered . 2025-11-20

    I was also struggling with having an image as an output and I found the solution. Skip the fullyConnectedLayer(n) part, because it causes the network to have an output of either a vector or just a number. However, make sure not to include any Pooling layers if you want your output to be of the same size as your input, because they cause downsampling. Finally, keep in mind that the number of filters of your final conv network is the same number as the 3rd dimension of your output image. For example,
    convolution2dLayer(3,1,'Padding','same') gives an output image of (M,N,1), where M,N are the rows and columns of the initial image. The next code runs to me.
     
    layers = [
    imageInputLayer([64 64 1])  % My initial image is 64x64

    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
%     averagePooling2dLayer(2,'Stride',1)  % Don't want this

    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
%     averagePooling2dLayer(2,'Stride',1) % Don't want this
  
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    convolution2dLayer(3,1,'Padding','same') % Because my initial image 3rd dimension is one.
    batchNormalizationLayer
    reluLayer
      
    dropoutLayer(0.2)
%     fullyConnectedLayer(10) % Don't want this
    regressionLayer];


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