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Yongcun Hu

Publications and source records attributed to Yongcun Hu.

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EV-NVC: Efficient Variable bitrate Neural Video Compression

Training neural video codec (NVC) with variable rate is a highly challenging task due to its complex training strategies and model structure. In this paper, we train an efficient variable bitrate neural video codec (EV-NVC) with the piecewise linear sampler (PLS) to improve the rate-distortion performance in high bitrate range, and the long-short-term feature fusion module (LSTFFM) to enhance the context modeling. Besides, we introduce mixed-precision training and discuss the different training strategies for each stage in detail to fully evaluate its effectiveness. Experimental results show that our approach reduces the BD-rate by 30.56% compared to HM-16.25 within low-delay mode.

cs.MM

Pixel super-resolved lensless on-chip sensor with scattering multiplexing

Lensless on-chip microscopy has shown great potential for biomedical imaging due to its large-area and high-throughput imaging capabilities. By combining the pixel super-resolution (PSR) technique, it can improve the resolution beyond the limit of the imaging detector. However, existing PSR techniques are restricted to the feature size and crosstalk of modulation components (such as spatial light modulator), which cannot efficiently encode target information. Besides, the reconstruction algorithms suffer from the trade-off between image quality, reconstruction resolution and computational efficiency. In this work, we constructed a novel integrated lensless on-chip sensor via scattering multiplexing, and reported a robust PSR algorithm for sample reconstruction. The sensor employed a scattering layer as a modulator, which was permanently integrated with the detector. Benefiting from the high-degree-of-freedom reconstruction of the scattering layer, we realized fine wavefront modulation with a small feature size. The integration engineering avoided repetitious calibration and reduce the measurement complexity. The reported PSR algorithm combines both model-driven and data-driven strategies to efficiently exploit the high-frequency information from the fine modulation. A series of experiments validated that the reported sensor provides a low-cost solution for large-scale microscopic imaging, with significant advantages in resolution, image contrast and noise robustness.

eess.IV