  1D Total Variation (TV) denoising, considering the data fidelity and the
Total Variation (TV) regularization, proposes a good restored signal preserving
shape edges. The main issue is how to choose the weight $\lambda$ balancing
those two terms. In practice, this parameter is selected by assessing a list of
candidates (e.g. cross validation), which is inappropriate for the real time
application. In this work, we revisit 1D Total Variation restoration algorithm
proposed by Tibshirani and Taylor. A heuristic method is integrated for
estimating a good choice of $\lambda$ based on the extremums number of restored
signal. We propose an offline version of restoration algorithm in O(n log n) as
well as its online implementation in O(n). Combining the rapid algorithm and
the automatic choice of $\lambda$, we propose a real-time automatic denoising
algorithm, providing a large application fields. The simulations show that our
proposition of $\lambda$ has a similar performance as the states of the art.
