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Creators/Authors contains: "Chan, Stanley"

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  1. ; ; (, IEEE)
    Free, publicly-accessible full text available September 14, 2026
  2. ; ; (, IEEE)
    Free, publicly-accessible full text available September 14, 2026
  3. ; ; ; (, IEEE)
    Free, publicly-accessible full text available September 14, 2026
  4. ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available September 14, 2026
  5. ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available June 10, 2026
  6. ; ; (, International Image Sensor Workshop)
    Imaging low-light high dynamic range (HDR) scenes in a single capture is challenging for conventional sensors when exposure bracketing is not feasible due to application constraints. Advancements in sensor technology have narrowed the gap, as split-pixel and dual conversion gain (DCG) enables single-frame HDR capture and Quanta Image Sensors (QIS) allow counting individual photons at low light. However, removing shot noise from a single HDR image remains a difficult task due to the spatially varying nature of noise. To address this issue, we propose a learnable pipeline with a modular design for processing high bit-depth QIS raw images. Compared to existing algorithmic solutions, our approach offers superior reconstruction performance and greater robustness to variations in illuminance and noise. 
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  7. ; ; (, SPIE)
    Bose-Pillai, Santasri R; Dolne, Jean J; Kalensky, Matthew (Ed.)
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  8. ; ; ; ; (, IEEE)
    Full Text Available 
  9. ; ; ; ; (, IEEE Transactions on Electron Devices)
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  10. ; ; ; ; ; (, IEEE)
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