Brown, Michael S.Yang, Beixuan2024-03-182024-03-182024-03-16https://hdl.handle.net/10315/41869Autoexposure (AE) is a critical step cameras apply to ensure properly exposed images. While current AE algorithms are effective in well-lit environments with unchanging illumination, these algorithms still struggle in environments with bright light sources or scenes with abrupt changes in lighting. A significant hurdle in developing new AE algorithms for challenging environments, especially those with time-varying lighting, is the lack of platforms to evaluate AE algorithms and suitable image datasets. To address this issue, we have designed a software platform allowing AE algorithms to be used in a plug-and-play manner with the dataset. In addition, we have captured a new 4D exposure dataset that provides a complete solution space (i.e., all possible exposures) over a temporal sequence with moving objects, bright lights, and varying lighting. Our dataset and associate platform enable repeatable evaluation of different AE algorithms and provide a much-needed starting point to develop better AE methods.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceElectronic Thesis or Dissertation2024-03-16Auto exposureComputer scienceComputer visionImage processingImage datasetCamera pipeline