Convolutions are used to scan an image and apply a filter to obtain a certain feature using a kernel matrix. An image kernel is a matrix that is used to apply effects such as blurring and sharpening. Kernels are used in machine learning for feature extraction—that is, selecting the most important pixels of an image. It also preserves the spatial relationship between pixels.
In the following screenshot, we can see that after applying kernels, the example image is transformed into feature maps:
In Fig 4.33, we can see how the convolution works. We have an example of a grayscale image, the blue box is the kernel, and the green box is the final image. In general, the kernel is applied to the entire image and scans the features of the image. Convolution can be used when generating a new image, scaling down the image, blurring the image, or sharpening the image, depending on the value of the kernel we use...