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Updated: Jan 8, 2026

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Learning a Domain-Specialized Network for Light Field Spatial-Angular Super-Resolution.

Yifan Mao, Xinpeng Huang, Yilei Chen

    IEEE Transactions on Visualization and Computer Graphics
    |December 17, 2025
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    Summary
    This summary is machine-generated.

    This study introduces a novel domain-specialized framework for light field (LF) super-resolution (SR). It achieves significant improvements in spatial and angular resolution with fewer parameters than existing methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Optics

    Background:

    • Light field (LF) imaging faces a trade-off between spatial resolution and angular sampling.
    • Existing spatial-angular super-resolution (SR) methods suffer from parameter redundancy and error accumulation.
    • Current end-to-end approaches lack domain-specific optimization for SR tasks.

    Purpose of the Study:

    • To propose a domain-specialized framework for joint spatial-angular super-resolution in light field imaging.
    • To enhance both spatial resolution and angular sampling density concurrently.
    • To overcome limitations of traditional and end-to-end SR methods.

    Main Methods:

    • Developed a domain-specialized framework with stage-tailored strategies for angular and spatial SR.
    • Introduced a cross-view consistency modulation module for angular SR, modeling long-range dependencies.
    • Proposed a detail-aware state space model for spatial SR and a cross-domain integration module for feature fusion.

    Main Results:

    • Achieved substantial improvements in both qualitative and quantitative comparisons on public LF datasets.
    • Demonstrated superior performance over state-of-the-art spatial-angular SR methods.
    • Reduced model parameters by approximately 50% compared to competing approaches.

    Conclusions:

    • The proposed domain-specialized framework effectively addresses the spatial-angular super-resolution challenge in light field imaging.
    • Stage-tailored strategies and domain-specific modules lead to synergistic optimization and improved performance.
    • The method offers a more efficient and effective solution for enhancing light field image quality.