Box blur kernel

1. What is blur kernels?
2. How does box blur work?
3. What is kernel in Gaussian blur?
4. What is the difference between Gaussian blur and box blur?

What is blur kernels?

The blur kernel de-emphasizes differences in adjacent pixel values. The emboss kernel (similar to the sobel kernel and sometimes referred to mean the same) givens the illusion of depth by emphasizing the differences of pixels in a given direction.

How does box blur work?

The Box Blur is an example of a convolution. It uses a sliding window the size of the kernel to calculate the average of a neighborhood of pixels. Using a sliding window, the convolution will process all of the pixels in the image. Once the convolution is completed, our new image will appear blurred.

What is kernel in Gaussian blur?

In a Gaussian blur, the pixels nearest the centre of the kernel are given more weight than those far away from the centre. The rate at which this weight diminishes is determined by a Gaussian function, hence the name Gaussian blur. A Gaussian function maps random variables into a normal distribution or “Bell Curve”.

What is the difference between Gaussian blur and box blur?

The central difference, depending on algorithm, is that Gaussian blur takes a weighted average around the pixel, while “normal” blur just averages all the pixels in the radius of the single pixel together (I believe). I think this latter “normal” blur is called box blur.