AbstractA method for obtaining the exact solution for the total variation denoising problem of piecewise constant images in dimension one is presented.
The validity of the algorithm relies on some results concerning the behavior of the solution when the parameter λ in front of the fidelity term varies.
Albeit some of them are well-known in the community, here they are proved with simple techniques based on qualitative geometrical properties of the solutions.
Artefacts removing (de-noising) from EEG signals has been an important aspect for medical practitioners for diagnosis of health issues related to brain. Several methods have been used in last few decades. Wavelet and total variation based de-noising have attracted the attention of engineers and scientists due to their de-noising efficiency. In this article, EEG signals have been de-noised using total variation based method and results obtained have been compared with the results obtained from the celebrated wavelet based methods . The performance of methods is measured using two parameters: signal-to-noise ratio and root mean square error. It has been observed that total variation based de-noising methods produce better results than the wavelet based methods.
Exact solutions for the total variation denoising problem of piecewise constant images in dimension one
