Should I apply image processing techniques to the inputs of convolution networks? After working for some time with feature-based pattern recognition, I am switching to CNN to see if I can get a higher recognition rate.

In my feature-based algorithm, I do some image processing on the picture before extracting the features, such as some convolution filters to reduce noise and segmentation into the foreground and background, and finally identifying and binarization of objects.

Should I do the same image processing before feeding data into my CNN, or is it possible to feed raw data to a CNN and expect that the CNN will adapt automatically without per-image-processing steps?
The whole interest of using deep learning-based solutions is that you don't have to do all those pre-processings, i.e. binarization, segmentation of background. CNNs, such as YOLO or FasterRCNN, can learn how to retrieve that information by themselves.