Haodong Pan

Haodong Pan, Feng Gao, Junyu Dong et al.

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global or spectral context information for HSI denoising. However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral structure preservation. In view of this, we propose a novel solution to investigate the HSI denoising using a Multi-scale Adaptive Fusion Network (MAFNet), which can learn the complex nonlinear mapping between clean and noisy HSI. Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module. Specifically, we first generate a set of multiscale images and feed them into a coarse-fusion network to exploit the contextual texture correlation. Thereafter, a fine fusion network is followed to exchange the information across the parallel multiscale subnetworks. Furthermore, we design a co-attention fusion module to adaptively emphasize informative features from different scales, and thereby enhance the discriminative learning capability for denoising. Extensive experiments on synthetic and real HSI datasets demonstrate that the proposed MAFNet has achieved better denoising performance than other state-of-the-art techniques. Our codes are available at \verb'https://github.com/summitgao/MAFNet'.

Haodong Pan, Hao Wei, Yusong Wang et al.

Learned image compression (LIC) has recently benefited from Transformer based and state space model (SSM) based architectures. Convolutional neural networks (CNNs) effectively capture local high frequency details, whereas Transformers and SSMs provide strong long range modeling capabilities but may cause structural information loss or ignore frequency characteristics that are crucial for compression. In this work we propose HCFSSNet, a Hybrid Convolution and Frequency State Space Network for LIC. HCFSSNet uses CNNs to extract local high frequency structures and introduces a Vision Frequency State Space (VFSS) block that models long range low frequency information. The VFSS block combines an Omni directional Neighborhood State Space (VONSS) module, which scans features horizontally, vertically and diagonally, with an Adaptive Frequency Modulation Module (AFMM) that applies content adaptive weighting of discrete cosine transform frequency components for more efficient bit allocation. To further reduce redundancy in the entropy model, we integrate AFMM with a Swin Transformer to form a Frequency Swin Transformer Attention Module (FSTAM) for frequency aware side information modeling. Experiments on the Kodak, Tecnick and CLIC Professional Validation datasets show that HCFSSNet achieves competitive rate distortion performance compared with recent SSM based codecs such as MambaIC, while using significantly fewer parameters. On Kodak, Tecnick and CLIC, HCFSSNet reduces BD rate over the VTM anchor by 18.06, 24.56 and 22.44 percent, respectively, providing an efficient and interpretable hybrid architecture for future learned image compression systems.

Haodong Pan, Yusong Wang, Nanning Zheng et al.

Geometric graph neural networks (GNNs) excel at capturing molecular geometry, yet their locality-biased message passing hampers the modeling of long-range interactions. Current solutions have fundamental limitations: extending cutoff radii causes computational costs to scale cubically with distance; physics-inspired kernels (e.g., Coulomb, dispersion) are often system-specific and lack generality; Fourier-space methods require careful tuning of multiple parameters (e.g., mesh size, k-space cutoff) with added computational overhead. We introduce Multi-stage Clustered Global Modeling (MCGM), a lightweight, plug-and-play module that endows geometric GNNs with hierarchical global context through efficient clustering operations. MCGM builds a multi-resolution hierarchy of atomic clusters, distills global information via dynamic hierarchical clustering, and propagates this context back through learned transformations, ultimately reinforcing atomic features via residual connections. Seamlessly integrated into four diverse backbone architectures, MCGM reduces OE62 energy prediction error by an average of 26.2%. On AQM, MCGM achieves state-of-the-art accuracy (17.0 meV for energy, 4.9 meV/Å for forces) while using 20% fewer parameters than Neural P3M. Code will be made available upon acceptance.