Cross-Layer Attention Feature Interaction and Multi-Scale Channel Attention Network for Single Image Dehazing

SUN Hang; FU Qiu-yue; LI Bo-hui; DAN Zhi-ping; YU Mei; WAN Jun

doi:10.12263/DZXB.20230821

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Cross-Layer Attention Feature Interaction and Multi-Scale Channel Attention Network for Single Image Dehazing

PAPERS | 更新时间：2025-12-11

- Cross-Layer Attention Feature Interaction and Multi-Scale Channel Attention Network for Single Image Dehazing
- ACTA ELECTRONICA SINICA Vol. 52, Issue 11, Pages: 3711-3726(2024)
- 作者机构：
  
  1.三峡大学计算机与信息学院，湖北宜昌 443000
  2.三峡大学湖北省水电工程智能视觉监测重点实验室，湖北宜昌 443000
  3.中南财经政法大学信息与安全工程学院，湖北武汉 430073
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62002233);Natural Science Foundation of Hubei Province of China(2021CFB004)
- DOI：10.12263/DZXB.20230821
  CLC： TP391.4
- Received：29 August 2023，
  
  Revised：2023-12-27，
  
  Published：25 November 2024
- 稿件说明：
移动端阅览
孙航, 付秋月, 李勃辉, 等. 基于跨层注意力特征交互和多尺度通道注意力的单幅图像去雾网络[J]. 电子学报, 2024, 52(11): 3711-3726.

SUN Hang, FU Qiu-yue, LI Bo-hui, et al. Cross-Layer Attention Feature Interaction and Multi-Scale Channel Attention Network for Single Image Dehazing[J]. Acta Electronica Sinica, 2024, 52(11): 3711-3726.
孙航, 付秋月, 李勃辉, 等. 基于跨层注意力特征交互和多尺度通道注意力的单幅图像去雾网络[J]. 电子学报, 2024, 52(11): 3711-3726. DOI：10.12263/DZXB.20230821

SUN Hang, FU Qiu-yue, LI Bo-hui, et al. Cross-Layer Attention Feature Interaction and Multi-Scale Channel Attention Network for Single Image Dehazing[J]. Acta Electronica Sinica, 2024, 52(11): 3711-3726. DOI：10.12263/DZXB.20230821

摘要

近年来，基于U型结构的卷积神经网络在去雾领域取得了显著的成果.然而，大多数基于U型结构的去雾网络将编码层特征直接传递到对应尺度的解码层，忽略了不同层次特征信息的有效利用.此外，去雾网络中广泛使用的通道注意力受感受野的限制，没有充分地利用上下文信息，从而对通道权重的学习起负面作用，使得重构的清晰图像不够理想.为了解决上述问题，本文提出了一种跨层注意力特征交互和多尺度通道注意力的去雾算法.具体来说，跨层注意力特征交互模块利用编码层的多尺度跨层特征学习层级权重，然后将这些跨层特征聚合传递到对应解码层，从而减少了去雾网络重构清晰图像过程中的特征稀释.此外，为了挖掘对于去雾网络非常重要的特征通道信息，本文设计了多尺度通道注意力机制，利用不同空洞率的空洞卷积提取多尺度特征信息，形成一个多尺度上下文并行学习的通道注意力机制，可以更有效地为去雾网络的特征分配权重.实验结果表明，本文提出的去雾算法在4个公开的数据集上相比现有的12种去雾方法取得了较好的客观评价指标和视

觉效果.本文的代码已上传至

https：//github.com/bohuisir/AAFMAN

https://github.com/bohuisir/AAFMAN

Abstract

In recent years

U-shaped convolutional neural networks (CNNs) have achieved remarkable progress in image dehazing. However

most U-shaped dehazing networks directly pass encoder features to the decoder at the corresponding scale

ignoring effective utilization of multi-scale features. In addition

channel attention widely used in dehazing networks is restricted by receptive fields

failing to sufficiently leverage contextual information

which adversely affects learning of channel weights. To address the above issues

this paper proposes a novel dehazing algorithm with cross-layer attentive feature interaction and multi-scale channel attention. Specifically

the cross-layer attentive feature interaction module learns hierarchical weights for multi-scale encoder features

and aggregates these cross-layer features for transfer to the decoder

thereby reducing feature dilution during the dehazing network's reconstruction of clear images. Moreover

to uncover channel information that is critical for dehazing networks

we devise a multi-scale channel attention mechanism that extracts multi-scale features by dilated convolutions with different dilation rates

forming a parallel learning scheme of channel attention with multi-scale contexts for more effective weight allocation for dehazing network features. Experimental results demonstrate that the proposed dehazing algorithm achieves better objective metrics and visual performance compared to 12 existing methods on 4 public datasets. The code for this paper has been uploaded to

http://github.com/bohuisir/AAFMAF

关键词

Keywords

references

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