Parallel Cross Strip Attention Network for Single Image Dehazing

TL;DR

This paper introduces a novel parallel stripe cross attention network with multi-scale strategies for single image dehazing, effectively capturing long-range dependencies and adapting to various blur sizes to produce clearer images.

Contribution

The proposed PCSA network combines multi-scale, channel-wise attention with adaptive weighting to improve dehazing performance over traditional models.

Findings

Enhanced dehazing quality demonstrated in experiments

Efficient long-range dependency modeling

Flexible adaptation to different blur sizes

Abstract

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.