<p>First, when you say <strong>transform</strong>, what does it mean? Is it something like a transform matrix, which can be represented as a set of parameters. Or it is actually very complicated, and can't be easily expressed by a few parameters. </p>

<p>For the former case, e.g. a perspective transform matrix, you can directly regress these parameters. In other words, you don't have to use an image as your network target. Instead, you can train a CNN to predict all matrix elements.</p>

<p>For the latter case, the short answer is YES. You should further read materials in special networks like <a href="https://arxiv.org/pdf/1506.02025.pdf" rel="nofollow noreferrer"><strong>spatial transformer networks</strong></a>. Note, I am not saying spatial transformer networks will solve your problem, but it points to the right direction, that is</p>

<ol>
<li>learn all transform related parameters in a network</li>
<li>transform your input image in a differentiable way (this might be a custom layer)</li>
<li>compute the reconstruction loss (e.g. MSE) between the transformed image and your target.</li>
</ol>
Keras : CNN for image processing or transform
<p>I am new  to the keras and machine learning. My research problem could definitely benefit from using  convolution neural networks (cnn).   I am trying to build a cnn for certain  image transformations specific to my research problem. So far most of the cnn examples i have come across are some form of classification. For the classification examples i understand the basic operation for a cnn, given an input image the network gives out a number. This number is compared with the label ( associated with the input image) and then the error from that is back-propagated to the network to adjust the weights for the next iteration. For my transformation problem, the output of the network is an image and the "label" which is the expected output  is also an image. This is where i am stuck. How to use an image as label, and what modifications i need to do in model.fit () to use image as a label. </p>

<p>Thank you and any guidance in this matter would be very much appreciated.
Best,
snsvsn</p>