# what the mean and median filters with an n x n kernel (neighborhood) do to the grey-levels of the image? [closed]

• sorry i haven't found a good title for these three questions i'm wondering if someone could give me explaination with proof (if possible ) of these three questions :

1. Given a grey-level image stored as a rectangular array of grey-level values, describe what the mean and median filters with an n x n kernel (neighborhood) do to the grey-levels of the image?
2. Describe the visual effect on an image of the mean and median filters. What happens when the size n of the neighborhood increases ?
3. Explain what are Gaussian noise and salt-and-pepper noise. Which type of filter is suitable for removing each type of noise ?
• Hello! We discourage posts that simply state a problem out of context, and expect the community to solve it. Assuming you tried to solve it yourself and got stuck, it may be helpful if you wrote your thoughts and what you could not figure out. It will definitely draw more answers to your post. Until then, the question will be voted to be closed / downvoted. You may also want to check out these hints, or use the search engine of this site to find similar questions that were already answered. – dkaeae Feb 26 '19 at 11:51

1. For each pixel in the original image, a 2n-1 x 2n-1 convolution matrix is centered on it and multiplied with the surrounding greyscale values, element-wise.

2. Mean and median filters result in an image blur, with the amount of blur increasing as n is increased.

3. Gaussian noise follows a normal distribution (the probability of a pixel changing by a certain value follows this distribution). Salt and pepper noise sometimes occurs from pixels being randomly set to near either their maximum (salt) or minimum (pepper) values. A mean filter might be more appropriate for gaussian noise, whereas a median filter helps ignore significant outliers (salt and pepper).