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A General Spatial-Frequency Learning Framework for Multimodal Image Fusion.

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    This study introduces SFINet, a novel network for multimodal image fusion that integrates spatial and frequency domains. SFINet enhances image quality in tasks like pan-sharpening and depth super-resolution by leveraging both local and global information.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Deep Learning

    Background:

    • Multimodal image fusion, including pan-sharpening and depth super-resolution, aims to generate high-resolution images by combining information from different sources.
    • Existing methods predominantly focus on spatial domain processing, neglecting the inherent frequency domain connections crucial for high-frequency information reconstruction.
    • This gap limits the potential for improved fusion performance by not fully exploiting complementary data characteristics.

    Purpose of the Study:

    • To address the limitations of spatial-domain-only methods in multimodal image fusion.
    • To propose novel solutions that effectively integrate both spatial and frequency domain information for enhanced image fusion.
    • To develop and validate a new network architecture, SFINet and its improved version SFINet++, for superior performance in image fusion tasks.

    Main Methods:

    • Devised the Spatial-Frequency Information Integration Network (SFINet) comprising spatial and frequency domain branches with dual-domain interaction.
    • The spatial branch utilizes spatial convolution-equipped invertible neural operators for local information integration.
    • The frequency branch employs modality-aware deep Fourier transformation for global contextual information capture; SFINet++ enhances spatial representation with information-lossless invertible neural operators.

    Main Results:

    • Extensive experiments demonstrate the effectiveness of SFINet and SFINet++ in multimodal image fusion.
    • The proposed networks achieve outstanding performance compared to state-of-the-art methods.
    • Validation was conducted on two representative tasks: pan-sharpening and depth super-resolution.

    Conclusions:

    • Integrating spatial and frequency domain information offers a significant advantage in multimodal image fusion.
    • SFINet and SFINet++ provide effective solutions for reconstructing high-frequency information and improving image quality.
    • The proposed dual-domain approach advances the field of image fusion, offering superior results in challenging applications.