The process of a Convolutional Neural Network (CNN) in image processing involves the following steps:

1. Data Preprocessing: The image is resized, normalized, and possibly padded to prepare it for processing.

2. Convolutional Layer: The image is convolved with a set of filters, scanning the image horizontally and vertically, to detect local features such as edges, lines, and textures.

3. Activation Function: The output from the convolutional layer is passed through an activation function, such as ReLU (Rectified Linear Unit) or Sigmoid, to introduce non-linearity.

4. Pooling Layer: The output is downsampled to reduce spatial dimensions and the number of parameters, using techniques like Max Pooling or Average Pooling.

5. Flattening: The output is flattened into a 1D array to prepare it for the fully connected layers.

6. Fully Connected Layers: The flattened output is passed through one or more fully connected layers, also known as dense layers, to classify the image.

7. Output Layer: The final output is produced, typically a probability distribution over possible classes.

This process allows CNNs to extract features from images, recognize patterns, and make predictions or classifications.