Extreme images refer to low-quality images taken under extreme environmental conditions such as haze, heavy rain, strong light, and shaking, which will lead to the failure of the visual system to effectively recognize the target. Most of the existing extreme image restoration algorithms only handle the restoration work of a certain type of image; how to effectively recognize all kinds of extreme images is still a challenge. Therefore, this paper proposes a classification and restoration algorithm for extreme images. Due to the large differences in the features on extreme images, it is difficult for the existing models such as DenseNet to effectively extract depth features. In order to solve the classification problem in the algorithm, we propose a Multicore Dense Connection Network (MDCNet). MDCNet is composed of dense part, attention part, and classification part. Dense Part uses two dense blocks with different convolution kernel sizes to extract features of different sizes; attention part uses channel attention mechanism and spatial attention mechanism to amplify the effective information in the feature map; classification part is mainly composed of two convolutional layers and two fully connected layers to extract and classify feature images. Experiments have shown that the recall of MDCNet can reach 92.75% on extreme image dataset. At the same time, the mAP value of target detection can be improved by about 16% after the image is processed by the classification and recovery algorithm.

中文翻译：

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基于多核密集连接网络的极限图像分类算法

极端图像是指在雾霾、大雨、强光、抖动等极端环境条件下拍摄的低质量图像，会导致视觉系统无法有效识别目标。现有的极限图像恢复算法大多只处理某类图像的恢复工作；如何有效识别各种极端图像仍然是一个挑战。因此，本文提出了一种极端图像的分类和恢复算法。由于极端图像上的特征差异较大，DenseNet等现有模型难以有效提取深度特征。为了解决算法中的分类问题，我们提出了多核密集连接网络（MDCNet）。MDCNet 由密集部分、注意力部分、和分类部分。Dense Part使用两个不同卷积核大小的dense blocks来提取不同大小的特征；注意力部分使用通道注意力机制和空间注意力机制来放大特征图中的有效信息；分类部分主要由两个卷积层和两个全连接层组成，对特征图像进行提取和分类。实验表明，MDCNet 在极端图像数据集上的召回率可以达到 92.75%。同时，图像经过分类恢复算法处理后，目标检测的mAP值可以提高16%左右。注意力部分使用通道注意力机制和空间注意力机制来放大特征图中的有效信息；分类部分主要由两个卷积层和两个全连接层组成，对特征图像进行提取和分类。实验表明，MDCNet 在极端图像数据集上的召回率可以达到 92.75%。同时，图像经过分类恢复算法处理后，目标检测的mAP值可以提高16%左右。注意力部分使用通道注意力机制和空间注意力机制来放大特征图中的有效信息；分类部分主要由两个卷积层和两个全连接层组成，对特征图像进行提取和分类。实验表明，MDCNet 在极端图像数据集上的召回率可以达到 92.75%。同时，图像经过分类恢复算法处理后，目标检测的mAP值可以提高16%左右。