# How can I approximate a signal

I have a signal as the below figure. I am looking for some schemes to approximate the signal. I have found some ways for that task:

1. Using basis function (polynomial) to approximate the signal
2. Divide the signal to many subrange, and assume that the signal in the range is constant.

Could you suggest to me the other way to approximate the signal? Thanks

• Why do you want to approximate the signal? And what features do you need to preserve? – Jazzmaniac Dec 20 '15 at 13:15
• The signal drawn from image intensity which contains bias. I want to remove bias, so I need to approximate it. I want to preserve edge feature – user3051460 Dec 20 '15 at 14:10
• it seems to me that you want to model the signal rather than approximate it. Eventhough one of their end results will be the same (such as producing an approximation of the signal waveform), they have quite different approaches to get that result. Therefore you need to find a "model" that produces a best approximation of the waveform as an AC + DC term... – Fat32 Dec 20 '15 at 19:23
• Right, Could you suggest to me for that task? – user3051460 Dec 21 '15 at 0:55
• If you are interested in just removing the bias, and you could assume the bias is fixed for wide range of samples than: you could do an FFT, delete the 0 frequency term and do an inverse FFT. – Moti Dec 21 '15 at 1:48

Other than different DC gains those above networks have the same frequency response. (Variable $\alpha$ is in the range of just greater than zero -to- one.) Note, those networks have very nonlinear phase response near zero Hz. If linear phase DC blocking is what you want then have a look at: http://www.dsprelated.com/showarticle/58.php