Haze obscures remote sensing images, hindering valuable information
extraction. To this end, we propose RSHazeNet, an encoder-minimal and
decoder-minimal framework for efficient remote sensing image dehazing.
Specifically, regarding the process of merging features within the same level,
we develop an innovative module called intra-level transposed fusion module
(ITFM). This module employs adaptive transposed self-attention to capture
comprehensive context-aware information, facilitating the robust context-aware
feature fusion. Meanwhile, we present a cross-level multi-view interaction
module (CMIM) to enable effective interactions between features from various
levels, mitigating the loss of information due to the repeated sampling
operations. In addition, we propose a multi-view progressive extraction block
(MPEB) that partitions the features into four distinct components and employs
convolution with varying kernel sizes, groups, and dilation factors to
facilitate view-progressive feature learning. Extensive experiments demonstrate
the superiority of our proposed RSHazeNet. We release the source code and all
pre-trained models at https://github.com/chdwyb/RSHazeNet.

提出了一种用于远程感知图像去雾的编码器极简和解码器极简框架 RSHazeNet，采用内层转置融合模块 (ITFM) 进行特征融合，采用跨层多视图交互模块 (CMIM) 和多视图渐进提取块 (MPEB) 增强特征学习，并通过大量实验证明了该方法的卓越性能。

遥感图像去雾的编码器最小化和解码器最小化框架

Encoder-minimal and Decoder-minimal Framework for Remote Sensing Image  Dehazing

Haze usually leads to deteriorated images with low contrast, color shift and
structural distortion. We observe that many deep learning based models exhibit
exceptional performance on removing homogeneous haze, but they usually fail to
address the challenge of non-homogeneous dehazing. Two main factors account for
this situation. Firstly, due to the intricate and non uniform distribution of
dense haze, the recovery of structural and chromatic features with high
fidelity is challenging, particularly in regions with heavy haze. Secondly, the
existing small scale datasets for non-homogeneous dehazing are inadequate to
support reliable learning of feature mappings between hazy images and their
corresponding haze-free counterparts by convolutional neural network
(CNN)-based models. To tackle these two challenges, we propose a novel two
branch network that leverages 2D discrete wavelete transform (DWT), fast
Fourier convolution (FFC) residual block and a pretrained ConvNeXt model.
Specifically, in the DWT-FFC frequency branch, our model exploits DWT to
capture more high-frequency features. Moreover, by taking advantage of the
large receptive field provided by FFC residual blocks, our model is able to
effectively explore global contextual information and produce images with
better perceptual quality. In the prior knowledge branch, an ImageNet
pretrained ConvNeXt as opposed to Res2Net is adopted. This enables our model to
learn more supplementary information and acquire a stronger generalization
ability. The feasibility and effectiveness of the proposed method is
demonstrated via extensive experiments and ablation studies. The code is
available at this https URL

该研究提出了一种新的两分支网络，利用 2D 离散小波变换、快速傅里叶卷积残差块和预训练的 ConvNeXt 模型来解决雾霾图像非均质去雾的问题，并进行了大量实验验证其有效性。

突破雾霾：基于快速傅里叶卷积和 ConvNeXt 的先进非均匀去雾方法

Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method  based on Fast Fourier Convolution and ConvNeXt

In recent years, adversarial attacks have drawn more attention for their
value on evaluating and improving the robustness of machine learning models,
especially, neural network models. However, previous attack methods have mainly
focused on applying some $l^p$ norm-bounded noise perturbations. In this paper,
we instead introduce a novel adversarial attack method based on haze, which is
a common phenomenon in real-world scenery. Our method can synthesize
potentially adversarial haze into an image based on the atmospheric scattering
model with high realisticity and mislead classifiers to predict an incorrect
class. We launch experiments on two popular datasets, i.e., ImageNet and
NIPS~2017. We demonstrate that the proposed method achieves a high success
rate, and holds better transferability across different classification models
than the baselines. We also visualize the correlation matrices, which inspire
us to jointly apply different perturbations to improve the success rate of the
attack. We hope this work can boost the development of non-noise-based
adversarial attacks and help evaluate and improve the robustness of DNNs.

提出了一种基于雾霾的新型对抗性攻击方法，利用大气散射模型，将潜在的对抗性雾霾合成到图像中，从而误导分类器，获得高成功率和更好的跨模型迁移性，并且还发现了联合应用不同扰动可提高攻击成功率的相关性矩阵。