Predicting student engagement by disability type at four-year baccalaureate higher education institutions using self-reported data
Abstract
The distribution of real camera sensor data is well approximated by Poisson, and the estimation of the light intensity signal from the Poisson count data plays a prominent role in digital imaging. It is highly desirable for imaging devices to carry the ability to assess the performance of Poisson image restoration. Drawing on a new category of image quality assessment called corrupted reference image quality assessment (CR-QA), we develop a computational technique for predicting the quality score of the popular structural similarity index (SSIM) without having the direct access to the ideal reference image. We verified via simulation that the CR-SSIM scores indeed agrees with the full reference scores; and the visually optimal denoising experiments performed on real camera sensor data give credibility to the impact CR-QA has on real imaging systems.
