The median filter is primarily used for noise removal from images and signals, especially "salt and pepper" type noise. It is also known to preserve edges well throughout noise removal. If preserving edges is important, it can be a good alternative to a convolution filter.
aishikbhattacharyya
When median filter is used with large kernel sizes, it can negatively impact the results because it over-smoothes the image, or removes potentially important details in the image. As a result, it's important to make sure what kernel size is being used based on how much detail is needed to be preserved.
srikartalluri
This is super important as it has several advantages. Outside the obvious noise reduction, it also has the ability to preserve edges (because it does not change edges thus maintaining sharp edges. Also due to its simple implementation, it also works relatively fast. However, it has some drawbacks as it sometimes removes artifacts such as fine dust or specks.
jacky-p
I have always wondered how noise reduction worked since there have be various times I've used such a feature in image editing programs. This median filter appears to be close to images I have worked with when using noise reduction. Of course to remove the noise it comes with the cost of losing some clarity creating a blurred affect on the image. I can see how the median filter can work better that the gaussian blur since it more evenly obtains information from neighboring pixels when compared to the weighted average of the surrounding pixels.
emily-xiao
It's worth noting that the median filter's non-linearity is what allows the median filter to excel in preserving edges while removing noise. Unlike linear filters that might blur edges by averaging pixels across boundaries, the median filter can maintain sharp transitions between distinct image regions.
zepluc
A larger filter size (10px median filter) results in a more pronounced blurring effect, while smaller sizes preserve more details.
The median filter is primarily used for noise removal from images and signals, especially "salt and pepper" type noise. It is also known to preserve edges well throughout noise removal. If preserving edges is important, it can be a good alternative to a convolution filter.
When median filter is used with large kernel sizes, it can negatively impact the results because it over-smoothes the image, or removes potentially important details in the image. As a result, it's important to make sure what kernel size is being used based on how much detail is needed to be preserved.
This is super important as it has several advantages. Outside the obvious noise reduction, it also has the ability to preserve edges (because it does not change edges thus maintaining sharp edges. Also due to its simple implementation, it also works relatively fast. However, it has some drawbacks as it sometimes removes artifacts such as fine dust or specks.
I have always wondered how noise reduction worked since there have be various times I've used such a feature in image editing programs. This median filter appears to be close to images I have worked with when using noise reduction. Of course to remove the noise it comes with the cost of losing some clarity creating a blurred affect on the image. I can see how the median filter can work better that the gaussian blur since it more evenly obtains information from neighboring pixels when compared to the weighted average of the surrounding pixels.
It's worth noting that the median filter's non-linearity is what allows the median filter to excel in preserving edges while removing noise. Unlike linear filters that might blur edges by averaging pixels across boundaries, the median filter can maintain sharp transitions between distinct image regions.
A larger filter size (10px median filter) results in a more pronounced blurring effect, while smaller sizes preserve more details.