Hierarchical Image Peeling: A Flexible Scale-space Filtering Framework

TL;DR

This paper introduces a flexible hierarchical image disassembly framework based on scale-space filtering, utilizing a recurrent network for efficient processing of high-resolution images in real-time, with demonstrated theoretical and experimental advantages.

Contribution

It proposes a novel formal definition of image disassembly and a scalable, flexible framework that outperforms existing methods in efficiency and effectiveness.

Findings

Framework handles 1080p images at over 60 fps

Outperforms state-of-the-art methods in accuracy and efficiency

Applicable to various practical scenarios

Abstract

The importance of hierarchical image organization has been witnessed by a wide spectrum of applications in computer vision and graphics. Different from image segmentation with the spatial whole-part consideration, this work designs a modern framework for disassembling an image into a family of derived signals from a scale-space perspective. Specifically, we first offer a formal definition of image disassembly. Then, by concerning desired properties, such as peeling hierarchy and structure preservation, we convert the original complex problem into a series of two-component separation sub-problems, significantly reducing the complexity. The proposed framework is flexible to both supervised and unsupervised settings. A compact recurrent network, namely hierarchical image peeling net, is customized to efficiently and effectively fulfill the task, which is about 3.5Mb in size, and can handle 1080p images in more than 60 fps per recurrence on a GTX 2080Ti GPU, making it attractive for practical use. Both theoretical findings and experimental results are provided to demonstrate the efficacy of the proposed framework, reveal its superiority over other state-of-the-art alternatives, and show its potential to various applicable scenarios. Our code is available at \url{https://github.com/ForawardStar/HIPe}.