Introduction

In the realm of signal processing, machine learning, and data analysis, the concept of padding is widely used. Padding involves adding extra values at the edges of a vector or matrix to extend its size. Symmetric padding, a specific type of padding, replicates the existing values in a mirror-like fashion. This blog post will introduce the concept of symmetric padding and discuss its applications.

Applications

Understanding Symmetric Padding

Symmetric padding involves extending a vector or matrix by copying its elements in a mirrored manner. For example, if we symmetrically pad the vector [1, 2, 3] by one element on each side, the result would be [2, 1, 2, 3, 2].

Applications of Symmetric Padding

Symmetric padding is often used in convolution operations in signal processing and deep learning to preserve the spatial dimensions of the data. Moreover, it is used in image processing for kernel operations like blurring, sharpening, and edge detection, where edge pixels need a surrounding context.

References

• Numpy Pad - SciPy
• Padding - Coursera