Learning Asymmetric and Local Features in Multi-Dimensional Data through Wavelets with Recursive Partitioning

TL;DR

This paper presents a Bayesian hierarchical wavelet framework for adaptively learning local and asymmetric features in multi-dimensional data, achieving scalable and effective image reconstruction.

Contribution

It introduces a novel probabilistic model that adaptively learns wavelet bases aligned with data geometry, enabling scalable and precise multi-dimensional data analysis.

Findings

Outperforms state-of-the-art methods in image reconstruction tasks.

Maintains linear computational complexity with data size.

Effectively captures local and asymmetric features in images.

Abstract

Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images. It requires methods that are sensitive to local details while fast enough to handle massive numbers of images of ever increasing sizes. We introduce a probabilistic model-based framework that achieves these objectives by incorporating adaptivity into discrete wavelet transforms (DWT) through Bayesian hierarchical modeling, thereby allowing wavelet bases to adapt to the geometric structure of the data while maintaining the high computational scalability of wavelet methods---linear in the sample size (e.g., the resolution of an image). We derive a recursive representation of the Bayesian posterior model which leads to an exact message passing algorithm to complete learning and inference. While our framework is applicable to a range of problems including multi-dimensional signal processing, compression, and structural learning, we illustrate its work and evaluate its performance in the context of image reconstruction using real images from the ImageNet database, two widely used benchmark datasets, and a dataset from retinal optical coherence tomography and compare its performance to state-of-the-art methods based on basis transforms and deep learning.