Originally this question was asked EE SE yesterday but then i saw a comment about DSP SE ,so i am posting my question again here ,in hope to get much better and relevant answers

Link of EE question is https://electronics.stackexchange.com/questions/497036/signal-processing-vs-image-processing

My question was/is

Although both are domains of EE, but what are differences between them? I am able to draw following conclusions from wikipedia article of dip and also from some other web reources

1) DIP(Digital image processing) is a subset of DSP(digital signal processing)

2)Because of above concept in (1), DIP only deals with 2 dimensional images/signals or in some case 3 dimensional images/signals(forexample RGB images) while in DSP there isn't any restriction on number of dimensions?

Please kindly guide me and correct me if i am wrong in my concepts/assumptions


2 Answers 2


"General" DSP (and signal processing in general) tends to deal much more with problems that can be solved with linear filters, or at least a preponderance of linear filtering, and there's a lot more content involving 1-D signals -- so you tend to see a lot of emphasis on frequency-domain analysis and FFT (and even some folks who can't seem to distinguish between typing 'fft' in Matlab and understanding DSP).

Image processing (video processing in particular) tends to deal with problems that must be solved with lots of nonlinear processing -- image segmentation, finding edges, etc. Even super-duper neural networks have significant nonlinearities. While there are linear operations (i.e., convolution, cross-correlation) these tend to be processor-intensive, and are used sparingly.

One can go through an entire career in DSP and not do image processing, and visa-versa (I'm a case in point, with around 30 years experience in DSP and digital control systems, yet with very little image processing under my belt).

About the only thing that is really common, besides the fact that you're taking in something you could call a "signal" and you're doing something to it you could call "processing", is that "plain old" DSP and image processing both reward the practitioner who's willing to do the hard math (or who's lucky enough to find the hard math easy).

It also helps to be able to make intuitive leaps to potential solutions, and then do the hard math to back-fill to determine that your solution will actually work, to refine it, and to be on a firm mathematical foundation for your next intuitive leap.

  • $\begingroup$ i've done DSP on audio signals. usually monophonic, but sometimes multi-channel. and i have done a very little amount of image synthesis (generating a bitmap image for either displaying a waveform or its spectrum). but i have never done image processing. never done video or still image processing. i have an idea how somethings are done, but i never done'd it. $\endgroup$ May 3, 2020 at 6:06

Both can be seen as conceptually children of signal processing.

signal processing is parent of:

  • DIP
  • DSP

now there are ways to deal with them DIP has images and so you have to segment them, know how to represent them, enhance or filter them using the data which is in some limits dependent on some specific use. You should read here for basics and this for advanced scientific research

when we talk about DSP the signals we start sampling, transforms, filtering now there concepts are useful in DIP too but there representation is different. DSP might have basic signals like square, rectangle, triangle, sine and signals which deal with communication like OFDM, QAM or speech signals which is again dependent on the application

so your 1st point is right! in your 2nd point you said there are no restrictions on dimensions which might be possible from a theoretical stand point, practical applications need to work with the limits of the device or the bandwidth available etc. Those are the corrections I had.

Now the comparisons are made are really crude and are for only to get an idea of what it deals with, I didn't discuss about how and stuff, that would require lot of time. Feel free to go through standard recommended texts to get an in depth view


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