I'm not sure if this is what you are looking for but I find Goodfellow's book a pretty good resource:
Goodfellow, specifically Section 2, Chapter 9 deals with convolutional neural networks: https://www.deeplearningbook.org/
'Pattern Recognition and Machine Learning' by Bishop Might contains a section (5.5.5, pg 267 onwards) as well as an exercise, and a general discussion about neural networks in image recognition.
If you edit your question to post a bit more detail, we can offer better answers, for example, what is about the convolutional layer? How it's implemented?
If you are looking for a more basic introduction to convolutional layers I would also suggest:
A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way gives a pretty general overview, starting at the difference between CNNs and ANNs and explains why CNNs are superior to ANNs (for certain problems). It also gives some details about how the convolution actually works.
Demystifying the transpose convolution explains the transpose convolution operation in the context of how a traditional convolution; this may not be relevant if you are strictly using CNNs and not transpose-CNNs.
Understanding of Convolutional Neural Network (CNN) — Deep Learning is quite similar to "A Comprehensive..." link above, but it also includes information about filtering and shows the effect that different filters have on an image, which is certainly very import to an understanding of why we use CNNs.
Building a Convolutional Neural Network (CNN) in Keras (or one of the other thousand similar pages) are pretty good for just starting out and building your own CNN classifier. You can also check out examples from Keras, e.g. CIFAR10 CNN, but these tend to give you a very little information about why they designed the network the way that they did.
If, on the other hand, you are looking for some more advanced resources, here are is one that springs to mind:
Deep Residual Learning for Image Recognition by He et al., deals with a major advance in image recognition, using Residual Networks (ResNet). This type of network has become pretty popular, so I highly recommend giving it a read.