Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions to exhibit similar patterns to the semanticly consistent regions. This requires a model with a strong capacity to capture long-range dependencies. Existing models struggle in this regard due to the slow growth of receptive field for Convolutional Neural Networks (CNNs) based methods and patch-level interactions in Transformer-based methods, which are ineffective for capturing long-range dependencies. Motivated by this, we propose SEM-Net, a novel visual State Space model (SSM) vision network, modelling corrupted images at the pixel level while capturing long-range dependencies (LRDs) in state space, achieving a linear computational complexity. To address the inherent lack of spatial awareness in SSM, we introduce the Snake Mamba Block (SMB) and Spatially-Enhanced Feed-forward Network. These innovations enable SEM-Net to outperform state-of-the-art inpainting methods on two distinct datasets, showing significant improvements in capturing LRDs and enhancement in spatial consistency. Additionally, SEM-Net achieves state-of-the-art performance on motion deblurring, demonstrating its generalizability. Our source code is available: https://github.com/ChrisChenl023/SEM-Net.

The fusion of hyperspectral and multispectral images involves integrating high spectral resolution hyperspectral image (HSI) and high spatial resolution multispectral image (MSI) to generate a HSI with high spatial and spectral resolution (HR-HSI). Existing HSI-MSI fusion methods primarily focus on information fusion within the spatial domain; however, few solutions have explored the employment of frequency analysis to enhance spatial resolution, limiting their capability for global perception. In this paper, we propose an efficient and novel paradigm for HSI-MSI fusion through the cross-frequency enhanced spatial-spectral fusion network, named CESFusion, exploring the complementary fusion of information between the spatial and frequency domains. Specifically, we first present the cross-frequency domain fusion module (CFFM) to perform global analysis through the Fourier transform and effectively integrate and enhance the frequency domain information from both HSI and MSI. Subsequently, we propose the spectral modeling module (SpeMM) based on state space model (SMM) to capture long-range spectral dependencies with linear complexity, and integrate it with the spatial residual block-based module (SRM) for joint spatial-spectral feature extraction. Finally, to enable sufficient interaction between the spatial and frequency domains, we adopt the cross-domain interaction module (CDIM), capturing and integrating complementary information from both domains. Moreover, a frequency-based loss function is purposely designed to further improve the restoration of global information. Extensive experiments conducted on both synthetic and real datasets demonstrate the superiority of our CESFusion, as evidenced by both quantitative and qualitative evaluation results.

Hyperspectral image and Multispectral image (HSI-MSI) fusion aims to fuse a registered high-resolution multi-spectral image (HR-MSI) with a low-resolution hyperspectral image (LR-HSI) to generate a spatially enhanced HSI with high spectral resolution. Current fusion methods often make insufficient utilization of spatial and spectral prior information, including spatial self-similarity and inter-spectral correlations, resulting in a degradation in image fidelity. Therefore, we propose a novel HSI-MSI fusion network, called DA-LGNet, designed to learn spatial-spectral priors with a dual-attention mechanism for feature enhancement and utilize the advantages of the large kernel attention for global and local feature representation. Specifically, the large kernel attention module (LKAM) can efficiently extract and integrate long-range dependencies and detailed textual information. The dual-attention enhancement module (DAEM) incorporates the position attention mechanism with the channel attention mechanism, flexibly capturing spatial and spectral prior information, which are crucial for restoring high-resolution HSI (HR-HSI). Extensive experiments on two datasets demonstrate that our DA-LGNet importantly outper-forms other state-of-the-art methods.

The fusion of a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) aims to synthesize a high-resolution hyperspectral image (HR-HSI), enabling a broader range of applications for hyperspectral images (HSIs). However, existing fusion methods struggle to capture both long-range dependencies and fine-grained spatial features, resulting in block artifacts and spatial distortions in the reconstructed HR-HSIs. Therefore, we introduce MLKAF-Net, a multiscale HSI-MSI fusion method, which effectively formulates cross-modality fused features in both spatial and spectral domains. MLKAF-Net mainly consists of three modules: the multiscale large kernel attention module (MLKAM), the spatial information aggregation module (SIAM), and the spectral attention module (SPAM). Specifically, the MLKAM incorporates a multiscale mechanism into the large kernel decomposition, adaptively capturing both long-range dependencies and local granular information. We develop the SIAM to establish the spatial quality of the reconstructed HR-HSIs by aggregating abundant spatial information. The SPAM introduces the channel attention to effectively mitigate spectral distortion through preserving beneficial spectral information. Extensive experiments demonstrate that our MLKAF-Net importantly enhances the fusion performance compared to state-of-the-art methods.