What exactly does it mean to perform Fourier decomposition on an image? What about an image has "frequencies" that we can determine or split up?
emmaloool
It seems like a Fourier decomposition is a technique useful for computer vision (image processing being a subset). I'm curious to see how it can be useful for graphics applications: how does its use case differ between computer vision and computer graphics, which can be seen as fields answering opposite questions (i.e., vision=pictures->abstract representations, graphics=abstract representations->pictures)? Is there a notion of an "inverse"?
rgrao
@ngandhi, any image can be decomposed into low, medium and high frequency images. Low frequency images have slowly varying components like patches of similar color/texture, high frequencies have fast varying components like edges and noise. So usually doing a "blurring" operation on an image with a box filter or a Gaussian filter will "filter" out the high frequency or noise components, resulting in an image with primarily low-frequency components. On the other hand, it is also possible to "sharpen" an image by enhancing these high-frequency edges or noise. You could look it up on Wikipedia, but this is a general explanation :)
What exactly does it mean to perform Fourier decomposition on an image? What about an image has "frequencies" that we can determine or split up?
It seems like a Fourier decomposition is a technique useful for computer vision (image processing being a subset). I'm curious to see how it can be useful for graphics applications: how does its use case differ between computer vision and computer graphics, which can be seen as fields answering opposite questions (i.e., vision=pictures->abstract representations, graphics=abstract representations->pictures)? Is there a notion of an "inverse"?
@ngandhi, any image can be decomposed into low, medium and high frequency images. Low frequency images have slowly varying components like patches of similar color/texture, high frequencies have fast varying components like edges and noise. So usually doing a "blurring" operation on an image with a box filter or a Gaussian filter will "filter" out the high frequency or noise components, resulting in an image with primarily low-frequency components. On the other hand, it is also possible to "sharpen" an image by enhancing these high-frequency edges or noise. You could look it up on Wikipedia, but this is a general explanation :)