Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing
Qiaosi Yi, Juncheng Li, Faming Fang, Aiwen Jiang, Guixu Zhang

TL;DR
This paper introduces a multi-scale topological neural network for single image dehazing that effectively extracts and fuses features at different scales, leading to improved detail recovery and robustness.
Contribution
It proposes a novel multi-scale topological network with adaptive feature modules for progressive dehazing, enhancing feature extraction and fusion capabilities.
Findings
Outperforms state-of-the-art dehazing methods in experiments
Demonstrates strong fault tolerance and robustness
Effectively recovers image details from hazy images
Abstract
Single image dehazing is a challenging ill-posed problem that has drawn significant attention in the last few years. Recently, convolutional neural networks have achieved great success in image dehazing. However, it is still difficult for these increasingly complex models to recover accurate details from the hazy image. In this paper, we pay attention to the feature extraction and utilization of the input image itself. To achieve this, we propose a Multi-scale Topological Network (MSTN) to fully explore the features at different scales. Meanwhile, we design a Multi-scale Feature Fusion Module (MFFM) and an Adaptive Feature Selection Module (AFSM) to achieve the selection and fusion of features at different scales, so as to achieve progressive image dehazing. This topological network provides a large number of search paths that enable the network to extract abundant image features as…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Image Fusion Techniques · Video Surveillance and Tracking Methods
MethodsFeature Selection
