Image Denoising Using Tensor Product Complex Tight Framelets with Increasing Directionality

TL;DR

This paper introduces a family of tensor product complex tight framelets with increasing directionality for improved image denoising, offering comparable or better performance than the dual tree complex wavelet transform (DTCWT).

Contribution

It proposes a novel family of tensor product complex tight framelets with adjustable directionality, enhancing image denoising beyond existing wavelet-based methods like DTCWT.

Findings

TPCTF_4 offers similar performance to DTCWT with 6 directions.

TPCTF_6 achieves better denoising results than DTCWT.

Using TPCTF_n as the first stage improves DTCWT performance.

Abstract

Tensor product real-valued wavelets have been employed in many applications such as image processing with impressive performance. Though edge singularities are ubiquitous and play a fundamental role in two-dimensional problems, tensor product real-valued wavelets are known to be only sub-optimal since they can only capture edges well along the coordinate axis directions. The dual tree complex wavelet transform (DTCWT), proposed by Kingsbury [16] and further developed by Selesnick et al. [24], is one of the most popular and successful enhancements of the classical tensor product real-valued wavelets. The two-dimensional DTCWT is obtained via tensor product and offers improved directionality with 6 directions. In this paper we shall further enhance the performance of DTCWT for the problem of image denoising. Using framelet-based approach and the notion of discrete affine systems, we shall propose a family of tensor product complex tight framelets TPCTF_n for all integers n>2 with increasing directionality, where n refers to the number of filters in the underlying one-dimensional complex tight framelet filter bank. For dimension two, such tensor product complex tight framelet TPCTF_n offers (n-1)(n-3)/2+4 directions when n is odd, and (n-4)(n+2)/2+6 directions when n is even. In particular, TPCTF_4, which is different to DTCWT in both nature and design, provides an alternative to DTCWT. Indeed, TPCTF_4 behaves quite similar to DTCWT by offering 6 directions in dimension two, employing the tensor product structure, and enjoying slightly less redundancy than DTCWT. When TPCTF_4 is applied to image denoising, its performance is comparable to DTCWT. Moreover, better results on image denoising can be obtained by using TPCTF_6. Moreover, TPCTF_n allows us to further improve DTCWT by using TPCTF_n as the first stage filter bank in DTCWT.