TL;DR
A gradient histogram-guided learning Transformer (GHLFormer) for remote sensing image dehazing is proposed, where a learnable gradient histogram representation is introduced to characterize local structural distributions and a gradient histogram-guided self-attention reorganizes feature interactions according to structural similarity.
With the rapid development of high-resolution remote sensing platforms and the growing demand for intelligent Earth observation, remote sensing image semantic segmentation has become a key technology for road extraction, urban–rural planning, disaster monitoring, and ecological assessment. However, complex remote sensi...
Remote sensing image super-resolution aims to reconstruct high-quality spatial details from low-resolution observations, which is essential for land-cover interpretation, object detection, and downstream geospatial analysis. However, existing convolutional neural network (CNN)- and Transformer-based methods may struggl...
A novel hierarchical bilinear scattering Transformer (HBS-Former) that jointly captures local frequency-domain details and global semantic context and a hierarchical bilinear fusion (HBF) mechanism is developed to explicitly model second-order interactions among hierarchical features.
DPSF-Net is proposed, a dual-prior spatial-frequency network built on MCAF-Net for real-world RSID that achieves state-of-the-art performance on the real-world RRSHID remote sensing image dehazing benchmark and remains competitive across multiple synthetic datasets.
The feature mapping stage is reconstructed to incorporate physical edge priors and frequency-domain attention-based denoising, and a Spatial Attention Wavelet Transform Feature Decomposer (SA-WTFD) is introduced in the frequency branch to adaptively suppress high-frequency background clutter and enhance discriminative...
A novel dual-stream perception interaction network, termed DSPI-Net, which dynamically integrates low-level spatial features and multi-scale features to efficiently restore realistic RS images, and ensures suitability for implementation on resource-constrained edge devices, meeting real-time processing requirements.
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