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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Interactive Spatial-Frequency Fusion Mamba for Multi-Modal Image Fusion.

Yixin Zhu, Long Lv, Pingping Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 16, 2026
    PubMed
    Summary

    This study introduces the Interactive Spatial-Frequency Fusion Mamba (ISFM) for multi-modal image fusion. ISFM enhances image fusion by interactively integrating spatial and frequency domain information, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Multi-Modal Image Fusion (MMIF) combines images from diverse sources to preserve details and information.
    • Existing methods often use basic spatial-frequency fusion without interactive enhancement.
    • Incorporating frequency domain information can improve spatial feature representation in MMIF.

    Purpose of the Study:

    • To propose a novel Interactive Spatial-Frequency Fusion Mamba (ISFM) framework for advanced MMIF.
    • To enhance feature extraction and fusion by integrating spatial and frequency domain information interactively.
    • To improve the performance of MMIF systems through a novel fusion strategy.

    Main Methods:

    • Developed a Modality-Specific Extractor (MSE) for efficient, long-range feature extraction.
    • Introduced Multi-scale Frequency Fusion (MFF) for adaptive integration of frequency components.
    • Proposed an Interactive Spatial-Frequency Fusion (ISF) module to guide spatial features with frequency information.

    Main Results:

    • The ISFM framework demonstrated superior performance across six MMIF datasets.
    • Experimental results confirmed the effectiveness of the proposed interactive fusion approach.
    • ISFM achieved better results compared to current state-of-the-art MMIF methods.

    Conclusions:

    • The proposed ISFM framework offers a significant advancement in Multi-Modal Image Fusion.
    • Interactive integration of spatial and frequency information is crucial for enhanced fusion performance.
    • ISFM provides a robust and effective solution for combining multi-modal image data.