Curvelet transform with learning-based tiling

作者：

Highlights：

• Wavelet and wavelet-like transforms typically divide the frequency plane in a systematic non-adaptive approach.

• A learning-based method for adapting frequency domain tilings using curvelets as the basis algorithm is presented.

• Our results establish numerical and visual performance advantages over the default curvelet transform.

摘要

Highlights•Wavelet and wavelet-like transforms typically divide the frequency plane in a systematic non-adaptive approach.•A learning-based method for adapting frequency domain tilings using curvelets as the basis algorithm is presented.•Our results establish numerical and visual performance advantages over the default curvelet transform.

论文关键词：Directional transforms,Curvelets,Signal representation,Sparsity,Denoising

论文评审过程：Received 1 March 2016, Revised 25 January 2017, Accepted 25 January 2017, Available online 27 January 2017, Version of Record 31 January 2017.

论文官网地址：https://doi.org/10.1016/j.image.2017.01.009