2021
DOI: 10.3390/sym13061083
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Principal Component Wavelet Networks for Solving Linear Inverse Problems

Abstract: In this paper we propose a novel learning-based wavelet transform and demonstrate its utility as a representation in solving a number of linear inverse problems—these are asymmetric problems, where the forward problem is easy to solve, but the inverse is difficult and often ill-posed. The wavelet decomposition is comprised of the application of an invertible 2D wavelet filter-bank comprising symmetric and anti-symmetric filters, in combination with a set of 1×1 convolution filters learnt from Principal Compone… Show more

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Cited by 5 publications
(1 citation statement)
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“…R poses penalty terms on the unknown latent x which is associated with the prior term P x (x) defined in Eq. 1. λ is a Lagrangian parameter, which can be determined manually or automatically [4], [45].…”
Section: Introductionmentioning
confidence: 99%
“…R poses penalty terms on the unknown latent x which is associated with the prior term P x (x) defined in Eq. 1. λ is a Lagrangian parameter, which can be determined manually or automatically [4], [45].…”
Section: Introductionmentioning
confidence: 99%