HDR image formation and display has been an argument of extreme interest even when digital cameras were not yet consumer products. While recent research in both fields has seen very interesting works, none is really revolutionary, since what goes on behind the scene has been left basically unchanged. In the image formation field in particular, a lot of energy has been spent so to solve the problems that arise when taking multiple exposures: illumination change, camera shake and in-scene movement. In this paper we approach HDR image formation from a different perspective, which tries to solve in one move all the mentioned problems. More specifically, we propose a method that is able to estimate missing exposures for HDR image formation starting from only one under-exposed shot. Estimation is done through artificial neural networks: the development of a mathematical model is a highly desirable, but time consuming task. The results are are very interesting, although not perfect, and suggest that further research might lead to a suitable solution.
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