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Related Experiment Video

Updated: Jul 25, 2025

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Dual-branch fusion model for lensless imaging.

Yinger Zhang, Zhouyi Wu, Yunhui Xu

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    |June 29, 2023
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    Summary

    This study introduces a novel parallel dual-branch fusion model for lensless camera image reconstruction, combining model-based and deep neural network approaches for superior performance. The new method significantly improves image quality in lensless imaging systems.

    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Image Processing

    Background:

    • Lensless cameras offer reduced size, weight, and cost compared to traditional lensed cameras.
    • Image reconstruction is a critical challenge in lensless imaging systems.
    • Current mainstream reconstruction methods include model-based approaches and data-driven deep neural networks (DNNs).

    Purpose of the Study:

    • To investigate the strengths and weaknesses of model-based and DNN-based reconstruction methods.
    • To propose a novel parallel dual-branch fusion model for enhanced lensless image reconstruction.
    • To validate the effectiveness of the proposed model in both simulation and a real-world lensless camera prototype.

    Main Methods:

    • A parallel dual-branch fusion model is proposed, integrating model-based and data-driven DNN branches.

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  • Two fusion architectures, Merger-Fusion-Model and Separate-Fusion-Model, are designed, with the latter using an attention module for adaptive weighting.
  • A novel UNet-FC network architecture is introduced into the data-driven branch to leverage lensless optics properties.

    • Main Results:

    • The dual-branch fusion model demonstrates superior performance over state-of-the-art methods.
    • Quantitative improvements include +2.95dB PSNR, +0.036 SSIM, and -0.0172 LPIPS on a public dataset.
    • Experimental validation using a lensless camera prototype confirms the method's effectiveness.

    Conclusions:

    • The proposed parallel dual-branch fusion model effectively enhances lensless image reconstruction.
    • Integrating model-based and data-driven techniques with novel network architectures yields significant improvements.
    • The method shows practical applicability in real-world lensless imaging systems.