Directional tensor product complex tight framelets with low redundancy

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

13 Scopus Citations
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Original languageEnglish
Pages (from-to)603-637
Journal / PublicationApplied and Computational Harmonic Analysis
Issue number2
Online published29 Jul 2015
Publication statusPublished - Sep 2016


Having the advantages of redundancy and flexibility, various types of tight frames have already shown impressive performance in applications such as image and video processing. For example, the undecimated wavelet transform, which is a particular case of tight frames, is known to have good performance for the denoising problem. Empirically, it is widely known that higher redundancy rate of a tight frame often leads to better performance in applications. The wavelet/framelet transform is often implemented in an undecimated fashion for the purpose of better performance in practice. Though high redundancy rate of a tight frame can improve performance in applications, as the dimension increases, it also makes the computational cost skyrocket and the storage of frame coefficients increase exponentially. This seriously restricts the usefulness of such tight frames for problems in moderately high dimensions such as video processing in dimension three. Inspired by the directional tensor product complex tight framelets TP-CTFm with m≥3 in [15,20] and their impressive performance for image processing in [20,33], in this paper we introduce directional tensor product complex tight framelets TP-CTFm (called reduced TP-CTFm) with low redundancy. Such TP-CTFm are particular examples of tight framelet filter banks with mixed sampling factors. In particular, we shall develop a directional tensor product complex tight framelet TP-CTF6 such that it performs nearly as well as the original TP-CTF6 in [20] for image/video denoising/inpainting but it has significantly lower redundancy rates than TP-CTF6 in every dimension. The TP-CTF6 in d dimensions not only offers good directionality as the original TP-CTF6 does but also has the low redundancy rate [Formula presented] (e.g., the redundancy rates are 2,2[Formula presented] and [Formula presented] for dimension d=1,…,5, respectively), in comparison with the redundancy rate 2d×[Formula presented] of TP-CTF6 in dimension d. Moreover, our numerical experiments on image/video denoising and inpainting show that the performance using our proposed TP-CTF6 is often comparable with or sometimes better than several state-of-the-art frame-based methods which have much higher redundancy rates than that of TP-CTF6 .

Research Area(s)

  • Directional complex tight framelets, Image and video denoising, Image and video inpainting, Redundancy rate, Tensor product, Tight framelet filter banks with mixed sampling factors