A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging

Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme.Comment: Some figures are reduced to comply with arxiv's size constraints. Full size images are available as HAL technical report hal-01107519v5, IEEE Transactions on Computational Imaging, 201

The apparatus of digital archaeology

Digital Archaeology is predicated upon an ever-changing set of apparatuses – technological, methodological, software, hardware, material, immaterial – which in their own ways and to varying degrees shape the nature of Digital Archaeology. Our attention, however, is perhaps inevitably more closely focussed on research questions, choice of data, and the kinds of analyses and outputs. In the process we tend to overlook the effects the tools themselves have on the archaeology we do beyond the immediate consequences of the digital. This paper introduces cognitive artefacts as a means of addressing the apparatus more directly within the context of the developing archaeological digital ecosystem. It argues that a critical appreciation of our computational cognitive artefacts is key to understanding their effects on both our own cognition and on the creation of archaeological knowledge. In the process, it defines a form of cognitive digital archaeology in terms of four distinct methods for extracting cognition from the digital apparatus layer by layer