  Optical coding is widely used in computational imaging systems and is a good
approach for designing vision systems. However, most coding methods are
developed assuming additive Gaussian noise, while modern optical imaging
systems are mainly affected by Poisson noise. Previous studies have highlighted
the significant differences between these noise models and proposed coding
optimization algorithms for image recovery under Poisson noise. They concluded
that the compressibility arising from data variance is crucial for image
recovery under Poisson noise. This makes a strong case for the design of
end-to-end vision systems that avoid image formation, since the data-driven
vision tasks, typically downstream of imaging, is more compressible than
imaging itself. In this project, we propose a coding strategy by jointly
optimizing an entire vision system, including measurement and inference, using
the classification accuracy as a metric. We demonstrate the importance of
incorporating Poisson noise in optimizing even the simplest vision systems and
propose an approach to achieve it.
