Calle Lejdfors

Adaptive enhancement and noise reduction in very low light-level video

A general methodology for noise reduction and contrast enhancement in very noisy image data with low dynamic range is presented. Video footage recorded in very dim light is especially targeted. Smoothing kernels that automatically adapt to the local spatio-temporal intensity structure in the image sequences are constructed in order to preserve and enhance fine spatial detail and prevent motion blur. In color image data, the chromaticity is restored and demosaicing of raw RGB input data is performed simultaneously with the noise reduction. The method is very general, contains few user-defined parameters and has been developed for efficient parallel computation using a GPU. The technique has been applied to image sequences with various degrees of darkness and noise levels, and results from some of these tests, and comparisons to other methods, are presented. The present work has been inspired by research on vision in nocturnal animals, particularly the spatial and temporal visual summation that allows these animals to see in dim light.

Implementing an embedded GPU language by combining translation and generation

Dynamic languages typically allow programs to be written at a very high level of abstraction. But their dynamic nature makes it very hard to compile such languages, meaning that a price has to be paid in terms of performance. However under certain restricted conditions compilation is possible. In this paper we describe how a domain specific language for image processing in Python can be compiled for execution on high speed graphics processing units. Previous work on similar problems have used either translative or generative compilation methods, each of which has its limitations. We propose a strategy which combine these two methods thereby achieving the benefits of both.