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Image quality recovery in binary ghost imaging by adding random noise.

Junhui Li, Dongyue Yang, Bin Luo

    Optics Letters
    |April 15, 2017
    PubMed
    Summary
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    Quantization reduces ghost imaging quality. Adding random noise through dithering before quantization effectively compensates for this image quality decline, even in binary sampling scenarios.

    Area of Science:

    • Computational imaging
    • Quantum optics

    Background:

    • Ghost imaging relies on sampling data, which can suffer from reduced quality due to quantization.
    • Quantization, especially with fewer bits, significantly degrades image fidelity.

    Purpose of the Study:

    • To investigate the impact of data quantization on ghost imaging.
    • To evaluate the effectiveness of dithering as a technique to mitigate image quality loss caused by quantization.

    Main Methods:

    • Simulated ghost imaging experiments with varying bit depths.
    • Application of dithering (adding random noise) prior to quantization.
    • Parameter optimization for dithering.

    Main Results:

    • Image quality in ghost imaging declines with decreased bit depth during quantization.

    Related Experiment Videos

  • Dithering effectively compensates for image quality degradation caused by quantization.
  • Dithering proves effective even in extreme cases like binary sampling.

    • Conclusions:

    • Quantization is a critical factor affecting ghost imaging performance.
    • Dithering is a viable and effective method for enhancing ghost imaging quality under quantization.
    • Optimized dithering parameters can restore image fidelity in low-bit scenarios.