2020/12/17 by Zhanhao Liu, Liu, Zhanhao, Marion Perrodin +6
Computer Science · Engineering · Mathematics · #Advanced Image Processing Techniques #FOS: Electrical engineering #FOS: Mathematics #Image and Signal Denoising Methods #Optimization and Control (math.OC) #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #eess.SP #electronic engineering #information engineering #math.OC
paper · pdf · doi:10.48550/arxiv.2012.09481
arxiv created 2020/12/17 · openalex publication_date 2020/12/17 · arxiv updated 2020/12/18 · openalex created_date 2020/12/21 · openalex updated_date 2026/07/28
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 λ 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 λ 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 λ, we propose a real-time automatic denoising algorithm, providing a large application fields. The simulations show that our proposition of λ has a similar performance as the states of the art.