The existing deep learning-based image dehazing algorithms commonly employ an encoder–decoder structure to learn a direct mapping from hazy images to haze-free images. However, these state-of-the-art methods often fail to consider the varying contents of hazy images across different scenes, resulting in unsatisfactory dehazing outcomes. To address this issue, this paper attempts to integrate a novel instance-aware subnet into the classic encoder–decoder structure in order to achieve a clear separation between figure and background, conducting the selective incorporation of instance features into the dehazing network. Specifically, we introduce a novel architecture called the hybrid residual attention network, which is capable of separately extracting full-image features and instance-level features. This architecture incorporates attention mechanisms and a multi-scale dilated convolution structure, enabling adaptive perception of haze density in different scenes. Additionally, we introduce a global feature fusion subnet that employs a pixel attention structure to fuse features from the entire image and multiple individual instances, thus being aware of instance features. Compared to existing methods, our approach offers a major advantage in accurately estimating the haze density of individual instances, reducing color distortion, and mitigating noise amplification in the output images. Experimental results demonstrate that our method outperforms existing methods across different evaluation metrics and testing benchmarks. Therefore, we believe that our method will serve as a valuable addition to the current collection of artificial intelligence models and will benefit engineering applications in video surveillance and high-level computer vision tasks.

中文翻译：

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实例感知的图像去雾

现有的基于深度学习的图像去雾算法通常采用编码器-解码器结构来学习从有雾图像到无雾图像的直接映射。然而，这些最先进的方法往往无法考虑不同场景中模糊图像的不同内容，导致去雾结果不令人满意。为了解决这个问题，本文尝试将一种新颖的实例感知子网集成到经典的编码器-解码器结构中，以实现图形和背景之间的清晰分离，将实例特征选择性地合并到去雾网络中。具体来说，我们引入了一种称为混合残差注意网络的新颖架构，它能够分别提取全图像特征和实例级特征。该架构结合了注意力机制和多尺度扩张卷积结构，能够自适应感知不同场景中的雾霾密度。此外，我们引入了一个全局特征融合子网，它采用像素注意结构来融合整个图像和多个单独实例的特征，从而了解实例特征。与现有方法相比，我们的方法在准确估计单个实例的雾度密度、减少颜色失真和减轻输出图像中的噪声放大方面具有主要优势。实验结果表明，我们的方法在不同的评估指标和测试基准上优于现有方法。因此，我们相信我们的方法将成为当前人工智能模型集合的有价值的补充，并将有利于视频监控和高级计算机视觉任务的工程应用。