Box blurs are commonly used to approximate gaussian blur. Box blur can be applied repeatedly to the same image to approx a gaussian blur. In some ways, this allows for more control over the quality of the blur. In general, both filters help with noise reduction.
ShonenMind
As @marivasq said, a box blur can be used to approximate a Gaussian blur, as it is considered to be a spatial domain linear filter (basically every pixel in the image is the average of all it's neighbors' pixels). The reason a box blur can be repeatedly applied to approximate a Gaussian blur is actually due to the central limit theorem, and the idea that as we take more and more samples (in this case, applying the box blur more and more times), the resulting image will eventually converge to the point where it looks exactly like what the image would've looked like if it had gone through a Gaussian blur.
dhruvchowdhary
Gaussian Blur gives images a soft blur, similar to what you see in photos, because it changes pixel brightness based on how close they are to each other. This is different from box blur, which treats all nearby pixels equally. It's cool to think about how changing the sigma value affects how blurry the picture looks.
ShivanPatel2025
@dhruvcowdhary yeah, its really cool to think about how useful this is too for photographers and graphic designers.
katherine-liu0
I found the discussion on Gaussian blurring very interesting because it connected the dots between the mathematical theory we learned and its practical application in smoothing out images. Seeing how different kernel sizes affect the blurring intensity gave me a much clearer understanding of how image processing works in software I use daily like Adobe Photoshop.
NothernSJTU
Gaussian blur helps reduce image noise by smoothing transitions.
The advantage of Gaussian blur lies in its mathematical properties and visual effects, making it a very practical tool in image processing.
stang085
I wonder what would happen if you were to increase the distances in the matrix, would it just start turning into a single color? Or would it be more circular and you would still see remnants of the weight in different places
NothernSJTU
A box blur is a simple image filtering technique where each pixel in the output image has a value equal to the average value of its neighboring pixels in the input image. The neighbors typically include the pixel itself and those in a surrounding box. This type of blur is characterized by a box-shaped convolution kernel.
JunoLee128
It's interesting how we abstract away this filter (or all filters including box, sobel, etc.) into a simple numerical matrix (don't have to calculate PDF or exponentiate every operation). Very efficent
Box blurs are commonly used to approximate gaussian blur. Box blur can be applied repeatedly to the same image to approx a gaussian blur. In some ways, this allows for more control over the quality of the blur. In general, both filters help with noise reduction.
As @marivasq said, a box blur can be used to approximate a Gaussian blur, as it is considered to be a spatial domain linear filter (basically every pixel in the image is the average of all it's neighbors' pixels). The reason a box blur can be repeatedly applied to approximate a Gaussian blur is actually due to the central limit theorem, and the idea that as we take more and more samples (in this case, applying the box blur more and more times), the resulting image will eventually converge to the point where it looks exactly like what the image would've looked like if it had gone through a Gaussian blur.
Gaussian Blur gives images a soft blur, similar to what you see in photos, because it changes pixel brightness based on how close they are to each other. This is different from box blur, which treats all nearby pixels equally. It's cool to think about how changing the sigma value affects how blurry the picture looks.
@dhruvcowdhary yeah, its really cool to think about how useful this is too for photographers and graphic designers.
I found the discussion on Gaussian blurring very interesting because it connected the dots between the mathematical theory we learned and its practical application in smoothing out images. Seeing how different kernel sizes affect the blurring intensity gave me a much clearer understanding of how image processing works in software I use daily like Adobe Photoshop.
Gaussian blur helps reduce image noise by smoothing transitions. The advantage of Gaussian blur lies in its mathematical properties and visual effects, making it a very practical tool in image processing.
I wonder what would happen if you were to increase the distances in the matrix, would it just start turning into a single color? Or would it be more circular and you would still see remnants of the weight in different places
A box blur is a simple image filtering technique where each pixel in the output image has a value equal to the average value of its neighboring pixels in the input image. The neighbors typically include the pixel itself and those in a surrounding box. This type of blur is characterized by a box-shaped convolution kernel.
It's interesting how we abstract away this filter (or all filters including box, sobel, etc.) into a simple numerical matrix (don't have to calculate PDF or exponentiate every operation). Very efficent