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Showing 1 to 5 of 5 for “"low-light imaging"”.

  1. Low noise cmos image sensors

    … but these systems tend to interfere with workflow. CMOS image sensors have been explored as a solution because of their high performance, low noise and small footprint. Reducing the noise floor on CMOS image sensors makes it possible to develop a general image sensor that can be used for …

    uiuc Repository record for Low noise cmos image sensors (opens in a new tab)

  2. Improving Extreme Low-light Image Denoising via Residual Learning

    Taking a satisfactory picture in a low-light environment remains a challenging problem. Low-light imaging mainly suffers from noise due to the low signal-to-noise ratio. Many methods have been proposed for the task of image denoising, but they fail to work with the noise under extremely low light

    umkc Repository record for Improving Extreme Low-light Image Denoising via Residual Learning (opens in a new tab)

  3. Quantum-mimetic imaging

    … have explored the use of nonclassical states of light to perform imaging or sensing. Although these experiments require quantum descriptions of light to explain their behavior, the advantages they claim are not necessarily unique to quantum light. This thesis explores the underlying principles …

    mit Repository record for Quantum-mimetic imaging (opens in a new tab)

  4. Energy-efficient circuits and systems for computational imaging and vision on mobile devices

    … These images are not merely projections of light from the scene onto the camera sensor but result from a deep calculation. This calculation involves a number of computational imaging algorithms such as high dynamic range (HDR) imaging, panorama stitching, image deblurring and low-light

    mit Repository record for Energy-efficient circuits and systems for computational imaging and vision on mobile devices (opens in a new tab)

  5. Image processing and synthesis: From hand-crafted to data-driven modeling

    … can generate high-quality natural images following the same distribution. We search the nearest neighbor in the latent space of the deep generate models using a weighted context loss and prior loss. This code is then converted to the clean and uncorrupted image of the input. Third, we study …

    uiuc Repository record for Image processing and synthesis: From hand-crafted to data-driven modeling (opens in a new tab)