Ms. Haoying Li Profile

Haoying Li

at Zhejiang Univ

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Publications (3)

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Proceedings Article | 24 November 2021 Paper

Imaging degradation evaluation on beam-splitting devices

Honghao Chen, Haoying Li, Huajun Feng, Zhihai Xu, Yueting Chen, Qi Li

Proceedings Volume 12065, 1206506 (2021) https://doi.org/10.1117/12.2601182

KEYWORDS: Imaging systems, Prisms, Optical simulations, Zemax, Imaging devices, Beam splitters, Reflection

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Researchers have proposed beam-splitting imaging methods to solve the problem that capturing the simultaneous view by multiple imaging instruments and positionally aligning them pixel by pixel. Beam-splitting prism and beam-splitting plate are two kinds of commonly used beam-splitting devices. However, which kind of the two devices degrade images to a less extent lacks discussion. Firstly, we theoretically analyzed possible image degradation caused by beam-splitting devices, which mainly include spherical aberration, ghost effect, a non-uniformity of the degradation function, and color cast. And we used ZEMAX optical simulation software to establish a beam-splitting imaging emulation experiment to simulate the image degradations mentioned above. we constructed an experience system of beam-splitting imaging in the laboratory. researchers could select a suitable device for their projects based on our study.

Proceedings Article | 24 November 2021 Paper

A plug-and-play demosaicing and denoising method on mobile cameras

Haoying Li, Honghao Chen, Meng Chang, Huajun Feng, Zhihai Xu, Qi Li, Yueting Chen

Proceedings Volume 12065, 1206537 (2021) https://doi.org/10.1117/12.2606869

KEYWORDS: Denoising, Cameras, RGB color model, Sensors, Signal to noise ratio, Image sensors, Image processing, Signal processing

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Image demosaicing and denoising are two important processes in the ISP pipeline of mobile cameras, because almost all mobile cameras in use today require colorful images generated by demosaicing algorithm, and the small sensor area of mobile cameras triggers low signal-to-noise ratio. Over the years, a considerable number of sequential demosaicing and denoising methods have been proposed, while they suﬀer from estimating the noise distribution and adjusting the hyper-parameters in order to balance demosaicing and denoising. There exit simultaneous demosaicing and denoising methods solving these problems. But they lack guidelines designed for mobile cameras. We propose a Plug-and-Play (PnP) demosaicing and denoising method on mobile cameras. Our method is built on PnP demosaicing framework which is derived from variable splitting theory. Any color demosaicing algorithm (i.e., bilinear, Malvar) can be plugged into our framework. We novelly trained an ISO conditioned denoiser for the framework and iteratively apply the denoiser in it. The ISO conditioned denoiser not only removes noise from the demosaicing procedure itself but also noise from camera sensors. By introducing ISO settings to the denoiser, our method takes possession of the adaptability and robustness in various capturing environments under diﬀerent camera settings. Our method has only two hyperparameters to tune, which eases the hyper-parameter adjustment in sequential demosaicing and denoising methods. Extensive experiments on synthetic datasets show that our method performs better than sequential demosaicing and denoising methods and is practical for mobile cameras.

Proceedings Article | 24 November 2021 Paper

A plug-and-play demosaicing and denoising method on mobile cameras

Haoying Li, Honghao Chen, Meng Chang, Huajun Feng, Zhihai Xu, Qi Li, Yueting Chen

Proceedings Volume 12065, 1206537 (2021) https://doi.org/10.1117/12.2606869

KEYWORDS: Denoising, Cameras, RGB color model, Sensors, Signal to noise ratio, Image sensors, Image processing, Signal processing

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