Proxy and Cross-Stripes Integration Transformer for Remote Sensing Image Dehazing

Existing Transformer-based dehazing methods for remote sensing (RS) images, to avoid quadratic computation complexity with respect to the feature map size, either perform self-attention mechanisms within local windows or capture long-range dependencies in the channel dimension rather than spatial. Each of these methods has its drawbacks. To address these limitations, we propose the Proxy and Cross-Stripes Integration Transformer (PCSformer) for RS image dehazing. PCSformer introduces two innovative Transformer blocks, i.e., sliding cross-stripes Transformer block and local proxy-based global Transformer block. The former allows us to directly model long-range dependencies and capture rich contextual information for large-scale objects in RS images. The latter seeks valuable information for thick haze regions within the whole feature map, generating more consistent and realistic scene details for such regions. Both achieve a large receptive field with cost-effective computational complexity within a single Transformer block. Furthermore, we introduce a shallow deep model with a small receptive field to conduct local refinement, which can mitigate artifacts associated with a large receptive field. Finally, to facilitate the better application of dehazing models to downstream visual tasks, we contribute two large-scale datasets for RS image dehazing. Experiments indicate that the dehazing models trained on our datasets can better assist downstream visual tasks under hazy atmospheric conditions compared to the dehazing models trained on existing datasets. Quantitative and qualitative experiments demonstrate that the proposed PCSformer significantly outperforms existing state-of-the-art techniques on dehazing benchmarks, particularly excelling in the restoration of thick haze scenes. The code and datasets are available at https://github.com/SmileShaun/PCSformer.