# Effect of ReLu derivative in convolution layer backpropagation

I'm trying to implement a CNN, as part of an academic project to learn how it works. The project is a SRCNN: a convolutional neural network that increase the resolution of images.

Following this review Review: SRCNN (Super Resolution), the network includes 2 convolution layers with ReLu activation and 1 convolution layer without ReLu activation. Feed forwarding is quite simple. I'm having trouble on the backpropagation though.

Following this article CNNs, Part 2: Training a Convolutional Neural Network, I got the following derivative to update filters weights :

$$\frac{\partial L}{\partial filter(x,y)}​​=\sum_{i=x}^{W-S+x}\sum_{j=y}^{H-S+y}\frac{\partial L}{\partial out(i,j)}*image(i+x,j+y)$$

where $$L$$ is the loss, $$filter(x,y)$$ the weight at the position $$(x,y)$$, $$W$$ the width of the image, $$H$$ the height of the image, $$S$$ the size of the filter.

But it does not take the activation function into account. It should work for the convolution layer without activation. But I can't see how to integrate ReLu derivative in this. And I didn't find any article that explains it clearly.