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
2 LP2N - Laboratoire Photonique, Numérique et Nanosciences
3 Department of Cognitive Neuroscience [Tubingen]

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Optique [physics.optics]

Antoine Lucat, Ramon Hegedus, Romain Pacanowski. Diffraction Prediction in HDR measurements. EUROGRAPHICS WORKSHOP ON MATERIAL APPEARANCE MODELING, Jun 2017, Helsinki, Finland. ⟨hal-01586466⟩