Diffraction Prediction in HDR measurements

Antoine Lucat 1, 2 Ramon Hegedus 3 Romain Pacanowski 1, 2
1 MANAO - Melting the frontiers between Light, Shape and Matter
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, LP2N - Laboratoire Photonique, Numérique et Nanosciences
Abstract : Modern imaging techniques have proved to be very efficient to recover a scene with high dynamic range values. However, this high dynamic range can introduce star-burst patterns around highlights arising from the diffraction of the camera aperture. The spatial extent of this effect can be very wide and alters pixels values, which, in a measurement context, are not reliable anymore. To address this problem, we introduce a novel algorithm that predicts, from a closed-form PSF, where the diffraction will affect the pixels of an HDR image, making it possible to discard them from the measurement. Our results give better results than common deconvolution techniques and the uncertainty values (convolution kernel and noise) of the algorithm output are recovered. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation—Line and curve generation In a wide variety of applications, the camera dynamic range does not permit to capture the whole dynamic range of the scene. High dynamic range (HDR) imaging [Rei10] is therefore necessary in order to fully recover the whole scene dynamic range. HDR photography merges photographies of a scene, taken at different levels of exposure, in order to increase the native camera dynamic range. HDR images are very useful because they speed up the acquisition process when using an imaging device. A common artifact arising from the high dynamic range is that star burst patterns can be seen around highlights. This effect is due to light diffraction through the lens diaphragm, and cannot be avoided. From a metrology perspective, these diffraction patterns pollute a lot of pixels around the highlights, which cannot be taken as reliable measurements. Since the camera diffraction pattern has a very high dynamic range, the higher the image dynamic range is, the more prominent is the pollution by diffraction. More generally, even if the effect becomes less visible, high-value pixels can always affect the lower value pixels through diffraction because the spatial range of diffraction is not bounded. One then has to be very careful when considering a low-value pixel as a reliable measurement. This diffraction effect can be described by a convolution, to which is added a classical measurement noise.
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Communication dans un congrès
EUROGRAPHICS WORKSHOP ON MATERIAL APPEARANCE MODELING, Jun 2017, Helsinki, Finland. 2017
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Antoine Lucat, Ramon Hegedus, Romain Pacanowski. Diffraction Prediction in HDR measurements. EUROGRAPHICS WORKSHOP ON MATERIAL APPEARANCE MODELING, Jun 2017, Helsinki, Finland. 2017. 〈hal-01586466〉

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