This thesis is devoted to edge detection from the raw image acquired by single-sensor cameras. Such cameras are fitted with Bayer Color Filter Array (CFA, generally Bayer one) and deliver raw CFA image, in which each pixel is characterized by only one out of the three colour components (red, green, or blue). A demosaicing procedure is necessary to estimate the other two missing colour components at each pixel, so as to obtain a colour image. This however produces artefacts that may affect the performance of low-level processing tasks applied to such estimated images. We propose to avoid demosaicing to compute partial derivatives for edge detection. Simple differentiation kernels, Deriche filters or shifted Deriche filters can be used either in a vector or a scalar approache. The vector approach computes partial derivatives for the three channels and the scalar approach first estimates a luminance image, then computes derivatives. The partial CFA derivatives are then used to compute Di Zenzo gradient for edge detection. We assess the performance of our methods on a large dataset of synthetic images with available edge ground truth. We show that CFA-based approaches may provide as accurate edge detection results as colour vector-based ones at much reduced computation cost.
Directeurs de thèse : Ludovic MACAIRE et Olivier LOSSON Rapporteurs : Hélène LAURENT et Jean-Christophe BURIE Examinateur : Philippe MONTESINOS
Thesis of the team Imagerie Couleur defended on 21/12/2017