The objective of single image dehazing is to restore hazy images and produce
clear, high-quality visuals. Traditional convolutional models struggle with
long-range dependencies due to their limited receptive field size. While
Transformers excel at capturing such dependencies, their quadratic
computational complexity in relation to feature map resolution makes them less
suitable for pixel-to-pixel dense prediction tasks. Moreover, fixed kernels or
tokens in most models do not adapt well to varying blur sizes, resulting in
suboptimal dehazing performance. In this study, we introduce a novel dehazing
network based on Parallel Stripe Cross Attention (PCSA) with a multi-scale
strategy. PCSA efficiently integrates long-range dependencies by simultaneously
capturing horizontal and vertical relationships, allowing each pixel to capture
contextual cues from an expanded spatial domain. To handle different sizes and
shapes of blurs flexibly, We employs a channel-wise design with varying
convolutional kernel sizes and strip lengths in each PCSA to capture context
information at different scales.Additionally, we incorporate a softmax-based
adaptive weighting mechanism within PCSA to prioritize and leverage more
critical features.

介绍了一种基于 Parallel Stripe Cross Attention (PCSA) 的多尺度策略的新型去雾网络，该网络能够高效地捕捉远距离依赖关系，并使用可变的卷积核尺寸和条形长度来处理不同大小和形状的模糊，同时在 PCSA 内部合并了基于 softmax 的自适应加权机制以优化特征的重要性。