In CNN, why do we increase the number of filters in deeper Convolution layers for complex images?
For this you need to understand what filters actually do.
Every layer of filters is there to capture patterns. For example, the first layer of filters captures patterns like edges, corners, dots etc. Subsequent layers combine those patterns to make bigger patterns (like combining edges to make squares, circles, etc.).
Now as we move forward in the layers, the patterns get more complex; hence there are larger combinations of patterns to capture. That's why we increase the filter size in subsequent layers to capture as many combinations as possible.