A physiological camera shake model for image stabilization systems

In order to design and validate optimal motion compensation controller algorithms for optical image stabilization, trustworthy hand tremor models are needed. This paper presents an accurate camera shake model derived from both medical and digital photography observations. It successfully reproduces subjective observations made about the shape and time evolution of hand tremor-induced motion blur patterns. Our model is compared to the simpler random-walk and straight-line-walk models as well as previously reported experiments. It paves the way towards accurate deblurring and optimal image stabilization algorithms by providing realistic motion blur point spread function matrices. To our best knowledge it is the first model in which both spectral and statistical characteristics fit several ground truths from literature.