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I think this was put very well in the well known "DSP guide" (chapter 24, section 5):

Fourier analysis is used in image processing in much the same way as with one-dimensional signals. However, images do not have their information encoded in the frequency domain, making the techniques much less useful. For example, when the Fourier transform is taken of an audio signal, the confusing time domain waveform is converted into an easy to understand frequency spectrum.

In comparison, taking the Fourier transform of an image converts the straightforward information in the spatial domain into a scrambled form in the frequency domain. In short, don't expect the Fourier transform to help you understand the information encoded in images.

So there is, of course, some structure and meaning behind the seemingly random pattern obtained by taking the DFT of a typical image (such as the example below), but it is not in a form that the human brain is prepared to understand intuitively, at least regarding visual perception.

Here is another interesting and quite readable exposition of what is contained in a Fourier transform of an image, and how it can be interpreted. It has a series of images that make it quite clear what the correspondence is between the Fourier-transformed and the original image.

edit: take also a look at this page, which demonstrates —near the end— how most of the perceptually important information of an image is stored in the phase (angle) component of the frequency representation.

I think this was put very well in the well known "DSP guide" (chapter 24, section 5):

Fourier analysis is used in image processing in much the same way as with one-dimensional signals. However, images do not have their information encoded in the frequency domain, making the techniques much less useful. For example, when the Fourier transform is taken of an audio signal, the confusing time domain waveform is converted into an easy to understand frequency spectrum.

In comparison, taking the Fourier transform of an image converts the straightforward information in the spatial domain into a scrambled form in the frequency domain. In short, don't expect the Fourier transform to help you understand the information encoded in images.

So there is, of course, some structure and meaning behind the seemingly random pattern obtained by taking the DFT of a typical image (such as the example below), but it is not in a form that the human brain is prepared to understand intuitively, at least regarding visual perception.

Here is another interesting and quite readable exposition of what is contained in a Fourier transform of an image, and how it can be interpreted. It has a series of images that make it quite clear what the correspondence is between the Fourier-transformed and the original image.

edit: take also a look at this page, which demonstrates —near the end— how most of the perceptually important information of an image is stored in the phase (angle) component of the frequency representation.

edit 2: another example of the meaning of phase and magnitude in the Fourier representation: "Section 3.4.1, Importance of phase and magnitude" of TU Delft's textbook "Fundamentals of Image Processing" demonstrates this quite clearly:

I think this was put very well in the well known "DSP guide" (chapter 24, section 5):

Fourier analysis is used in image processing in much the same way as with one-dimensional signals. However, images do not have their information encoded in the frequency domain, making the techniques much less useful. For example, when the Fourier transform is taken of an audio signal, the confusing time domain waveform is converted into an easy to understand frequency spectrum.

In comparison, taking the Fourier transform of an image converts the straightforward information in the spatial domain into a scrambled form in the frequency domain. In short, don't expect the Fourier transform to help you understand the information encoded in images.

So there is, of course, some structure and meaning behind the seemingly random pattern obtained by taking the DFT of a typical image (such as the example below), but it is not in a form that the human brain is prepared to understand intuitively, at least regarding visual perception.

Here is another interesting and quite readable exposition of what is contained in a Fourier transform of an image, and how it can be interpreted. It has a series of images that make it quite clear what the correspondence is between the Fourier-transformed and the original image.

edit: take also a look at this page, which demonstrates —near the end— how most of the perceptually important information of an image is stored in the phase (angle) component of the frequency representation.

I think this was put very well in the well known "DSP guide" (chapter 24, section 5):

Fourier analysis is used in image processing in much the same way as with one-dimensional signals. However, images do not have their information encoded in the frequency domain, making the techniques much less useful. For example, when the Fourier transform is taken of an audio signal, the confusing time domain waveform is converted into an easy to understand frequency spectrum.

In comparison, taking the Fourier transform of an image converts the straightforward information in the spatial domain into a scrambled form in the frequency domain. In short, don't expect the Fourier transform to help you understand the information encoded in images.

So there is, of course, some structure and meaning behind the seemingly random pattern obtained by taking the DFT of a typical image (such as the example below), but it is not in a form that the human brain is prepared to understand intuitively, at least regarding visual perception.

Here is another interesting and quite readable exposition of what is contained in a Fourier transform of an image, and how it can be interpreted. It has a series of images that make it quite clear what the correspondence is between the Fourier-transformed and the original image.

edit: take also a look at this page, which demonstrates —near the end— how most of the perceptually important information of an image is stored in the phase (angle) component of the frequency representation.

I think this was put very well in the well known "DSP guide" (chapter 24, section 5):

Fourier analysis is used in image processing in much the same way as with one-dimensional signals. However, images do not have their information encoded in the frequency domain, making the techniques much less useful. For example, when the Fourier transform is taken of an audio signal, the confusing time domain waveform is converted into an easy to understand frequency spectrum.

In comparison, taking the Fourier transform of an image converts the straightforward information in the spatial domain into a scrambled form in the frequency domain. In short, don't expect the Fourier transform to help you understand the information encoded in images.

So there is, of course, some structure and meaning behind the seemingly random pattern obtained by taking the DFT of a typical image (such as the example below), but it is not in a form that the human brain is prepared to understand intuitively, at least regarding visual perception.

Here is another interesting and quite readable exposition of what is contained in a Fourier transform of an image, and how it can be interpreted. It has a series of images that make it quite clear what the correspondence is between the Fourier-transformed and the original image.

I think this was put very well in the well known "DSP guide" (chapter 24, section 5):

Fourier analysis is used in image processing in much the same way as with one-dimensional signals. However, images do not have their information encoded in the frequency domain, making the techniques much less useful. For example, when the Fourier transform is taken of an audio signal, the confusing time domain waveform is converted into an easy to understand frequency spectrum.

In comparison, taking the Fourier transform of an image converts the straightforward information in the spatial domain into a scrambled form in the frequency domain. In short, don't expect the Fourier transform to help you understand the information encoded in images.

So there is, of course, some structure and meaning behind the seemingly random pattern obtained by taking the DFT of a typical image (such as the example below), but it is not in a form that the human brain is prepared to understand intuitively, at least regarding visual perception.

Here is another interesting and quite readable exposition of what is contained in a Fourier transform of an image, and how it can be interpreted. It has a series of images that make it quite clear what the correspondence is between the Fourier-transformed and the original image.

edit: take also a look at this page, which demonstrates —near the end— how most of the perceptually important information of an image is stored in the phase (angle) component of the frequency representation.