A1 Journal article (refereed)
Image inpainting using directional wavelet packets originating from polynomial splines (2021)

Averbuch, A., Neittaanmäki, P., Zheludev, V., Salhov, M., & Hauser, J. (2021). Image inpainting using directional wavelet packets originating from polynomial splines. Signal Processing: Image Communication, 97, Article 116334. https://doi.org/10.1016/j.image.2021.116334

JYU authors or editors

Publication details

All authors or editors: Averbuch, Amir; Neittaanmäki, Pekka; Zheludev, Valery; Salhov, Moshe; Hauser, Jonathan

Journal or series: Signal Processing: Image Communication

ISSN: 0923-5965

eISSN: 1879-2677

Publication year: 2021

Volume: 97

Article number: 116334

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.image.2021.116334

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/76200

Publication is parallel published: https://arxiv.org/abs/2001.04899


The paper presents a new algorithm for the image inpainting problem. The algorithm uses a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. For example, a set of the fourth-level qWPs comprises 62 different directions. The properties of these qWPs such as refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies, make them efficient in image processing applications, in particular, in dealing with the inpainting problem addressed in the paper. The obtained results for this problem are quite competitive with the best state-of-the-art algorithms. The inpainting is implemented by an iterative scheme, which expands the Split Bregman Iteration (SBI) procedure by supplying it with an adaptive variable soft thresholding based on the Bivariate Shrinkage algorithm. In the inpainting experiments, performance comparison between the qWP-based methods and the state-of-the-art algorithms is presented.

Keywords: image processing; signal processing; algorithms

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Last updated on 2021-09-08 at 13:03