Is there a rule of thumb for zero padding in image processing?

1. What does zero padding do to an image?
2. Why is it required to do zero padding in DFT analysis?
3. Why zero padding is done prior to filtering?
4. What is the effect of zero padding in frequency domain?

What does zero padding do to an image?

Padding is a term relevant to convolutional neural networks as it refers to the amount of pixels added to an image when it is being processed by the kernel of a CNN. For example, if the padding in a CNN is set to zero, then every pixel value that is added will be of value zero.

Why is it required to do zero padding in DFT analysis?

Zero padding enables you to obtain more accurate amplitude estimates of resolvable signal components. On the other hand, zero padding does not improve the spectral (frequency) resolution of the DFT. The resolution is determined by the number of samples and the sample rate.

Why zero padding is done prior to filtering?

Zero-padding allows space for this wrap-around to occur without contaminating actual output pixels.

What is the effect of zero padding in frequency domain?

In this case, we can say “zero padding in the frequency domain results in an increased sampling rate in the time domain”.