Du capteur à l'image couleur

This chapter deals with the acquisition of colour images by mono-CCD colour cameras. These devices acquire only one colour component at each pixel through the CFA (Colour Filter Array) that covers the CCD sensor. A procedure - called demosaicing - is necessary to estimate the other two missing colour components at each pixel to obtain a colour image. A mathematical formalization for demosaicing is proposed, before we present some of the methods in the demosaicing literature, as well as the post-processing algorithms to correct the estimated images. We then present objective criteria for the quality evaluation of colour images, and apply them to estimated images to draw up conclusions about the selection of a demosaicing method. At last, we examine why and how white balancing is required and implemented to provide images faithfull to the observed scene under varying illumination conditions

Informative sensing : theory and applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 145-156).Compressed sensing is a recent theory for the sampling and reconstruction of sparse signals. Sparse signals only occupy a tiny fraction of the entire signal space and thus have a small amount of information, relative to their dimension. The theory tells us that the information can be captured faithfully with few random measurement samples, even far below the Nyquist rate. Despite the successful story, we question how the theory would change if we had a more precise prior than the simple sparsity model. Hence, we consider the settings where the prior is encoded as a probability density. In a Bayesian perspective, we see the signal recovery as an inference, in which we estimate the unmeasured dimensions of the signal given the incomplete measurements. We claim that good sensors should somehow be designed to minimize the uncertainty of the inference. In this thesis, we primarily use Shannon's entropy to measure the uncertainty and in effect pursue the InfoMax principle, rather than the restricted isometry property, in optimizing the sensors. By approximate analysis on sparse signals, we found random projections, typical in the compressed sensing literature, to be InfoMax optimal if the sparse coefficients are independent and identically distributed (i.i.d.). If not, however, we could find a different set of projections which, in signal reconstruction, consistently outperformed random or other types of measurements. In particular, if the coefficients are groupwise i.i.d., groupwise random projections with nonuniform sampling rate per group prove asymptotically Info- Max optimal. Such a groupwise i.i.d. pattern roughly appears in natural images when the wavelet basis is partitioned into groups according to the scale. Consequently, we applied the groupwise random projections to the sensing of natural images. We also considered designing an optimal color filter array for single-chip cameras. In this case, the feasible set of projections is highly restricted because multiplexing across pixels is not allowed. Nevertheless, our principle still applies. By minimizing the uncertainty of the unmeasured colors given the measured ones, we could find new color filter arrays which showed better demosaicking performance in comparison with Bayer or other existing color filter arrays.by Hyun Sung Chang.Ph.D

Practical Implementation of LMMSE Demosaicing Using Luminance and Chrominance Spaces

Most digital color cameras sample only one color at each spatial location, using a single sensor coupled with a color filter array (CFA). An interpolation step called demosaicing (or demosaicking) is required for rendering a color image from the acquired CFA image. Already proposed Linear Minimum Mean Square Error (LMMSE) demosaicing provides a good tradeoff between quality and computational cost for embedded systems. In this paper we propose a modification of the stacked notation of superpixels, which allows an effective computing of the LMMSE solution from an image database. Moreover, this formalism is used to decompose the CFA sampling into a sum of a luminance estimator and a chrominance projector. This decomposition allows interpreting estimated filters in term of their spatial and chromatic properties and results in a solution with lower computational complexity than other LMMSE approaches for the same quality