Image Processing: Convolution vs Filtering

In summary, convolution and filtering are two methods used in image processing to calculate a 'dot-product' type calculation and place the value in the corresponding pixel of the output image. While filtering involves passing a mask over the image, convolution first flips the mask and then passes it over the image. Both methods are useful in practice and can be achieved through an FFT-based approach as well.
  • #1
Master1022
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Homework Statement
What is the intuitive difference in terms of use case for the convolution operation as opposed to filtering?
Relevant Equations
Convolution and filtering of images
Hi,

So my question is perhaps better asked as:
- What is the point of convolution (in 2D image processing)?
- Why would we use that operation in image processing?
- What is so special about that flipped version of the kernel?

Context:

In an image processing class, I was learning about the operations of convolution and filtering. This post here basically summarises my current knowledge of the differences between the two operations.
- Filtering: we pass a mask over an image and calculate a 'dot-product' type calculation at each point and place that value in the corresponding pixel of the output image

- Convolution: First we flip the mask both top to bottom and left to right and then pass that over the image. As above, we calculate a 'dot-product' type calculation and put that into the corresponding pixel of the output.

I struggle to see why this operation is useful in practice.

Any advice is greatly appreciated. Also, please do let me know if another forum was more suitable for this post - I was unsure of where to put this information engineering question.
 
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  • #2
Given your description of filtering and convolution, they are equivalent.
But there is another method for doing the same thing: Perform an FFT on the image and the filter; Multiply the two FFTs together to generate a new FFT; Perform an inverse FFT on the result.
 

Related to Image Processing: Convolution vs Filtering

What is the difference between convolution and filtering in image processing?

Convolution and filtering are both techniques used in image processing to alter or enhance an image. The main difference between the two is that convolution applies a mathematical operation to the entire image, while filtering only applies the operation to specific parts of the image. Convolution is more commonly used for image enhancement, while filtering is often used for noise reduction or feature extraction.

How does convolution work in image processing?

Convolution involves applying a mathematical operation, typically a kernel or filter, to each pixel in an image. The operation is performed by multiplying the values of the kernel with the corresponding pixel values in the image and then summing the results. This process is repeated for every pixel in the image, resulting in a new image with enhanced or altered features.

What is a filter in image processing?

A filter in image processing is a small matrix of numbers that is applied to an image to alter or enhance specific features. Filters can be designed for different purposes, such as blurring, sharpening, edge detection, or noise reduction. They can also be customized to achieve specific effects or to target certain features in an image.

What are the advantages of using convolution in image processing?

Convolution is a powerful technique in image processing because it can be used to enhance or alter specific features in an image without affecting the entire image. This allows for more precise and targeted adjustments, making it a useful tool for tasks such as image restoration, noise reduction, and feature extraction.

Can convolution and filtering be used together in image processing?

Yes, convolution and filtering can be used together in image processing. In fact, many image processing techniques involve a combination of both convolution and filtering to achieve desired results. For example, a filter may be applied to an image first to remove noise, and then convolution may be used to enhance specific features in the noise-free image.

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