Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints

This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results. More precisely, the texture synthesis is regarded as a constrained optimization problem, with constraints conditioning both the Fourier spectrum and statistical features learned by CNNs. In contrast with existing methods, the presented method inherits from previous CNN approaches the ability to depict local structures and fine scale details, and at the same time yields coherent large scale structures, even in the case of quasi-periodic images. This is done at no extra computational cost. Synthesis experiments on various images show a clear improvement compared to a recent state-of-the art method relying on CNN constraints only

Numerical modeling of micro-scale wind-induced pollutant dispersion in the built environment

Despite recent efforts directed towards the development of cleaner and more efficient energy sources, air pollution remains a major problem in many large cities worldwide, with negative consequences for human health and comfort. If the transport of pollutants by wind in urban areas can be predicted in an accurate way, remedial measures can be implemented and the exposure of people and goods to pollution can be decreased to limit these negative effects. This prediction can be achieved by experimental techniques, on-site or in wind tunnels, but also numerically, with the use of Computational Fluid Dynamics (CFD).In this thesis CFD is used to simulate wind-induced pollutant dispersion in the built environment. The accuracy of this approach in terms of pollutant concentration prediction always needs to be assessed. The reason is twofold. First, the wind flow around buildings is turbulent and cannot be solved exactly with CFD. This type of flow must therefore be approximated with so-called turbulence models. Second, various types of errors are present in the numerical solution and can affect its accuracy. The Reynolds-Averaged Navier-Stokes (RANS) and Large-Eddy Simulation (LES) turbulence modeling approaches are the most widely used in computational wind engineering. They are compared in this thesis, and evaluated by comparison with reference wind-tunnel experiments. In the first part, several generic cases of simplified isolated buildings are considered and, in the second part, an applied case of pollutant dispersion in an actual urban area (part of downtown Montreal) is studied. In the computations, care is taken to accurately simulate three key aspects of urban pollutant dispersion: (1) the atmospheric boundary layer flow, (2) the wind flow around buildings, and (3) the dispersion process. On average, the transport of pollutants by wind can be seen as the combination of, on the one hand, the transport by the mean flow and, on the other hand, the transport by the turbulent fluctuations. This decomposition is used here to evaluate the RANS – with various turbulence models – and LES approaches. Overall, the better performance of LES in terms of flow and concentration field prediction is demonstrated. In addition, LES has the advantage to provide the time-resolved velocity and concentration fields. Given the good accuracy of LES, this approach is used to investigate the physical mechanism of pollutant dispersion for the case of a simplified isolated building. The vortical structures present in the shear layers developing from the roof and sides of the building are shown to play a crucial role in the turbulent mass transport process. LES used as a research tool also allows evaluating models employed with RANS for turbulent mass transport, which is often assumed to act as a diffusion mechanism. The results of this study show that this hypothesis is not always valid and in some cases the turbulent mass flux in the streamwise direction is directed from the low to high levels of mean concentration (counter-gradient diffusion)

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

A geometrically aware auto-encoder for multi-texture synthesis

We propose an auto-encoder architecture for multi-texture synthesis. The approach relies on both a compact encoder accounting for second order neural statistics and a generator incorporating adaptive periodic content. Images are embedded in a compact and geometrically consistent latent space, where the texture representation and its spatial organisation are disentangled. Texture synthesis and interpolation tasks can be performed directly from these latent codes. Our experiments demonstrate that our model outperforms state-of-the-art feed-forward methods in terms of visual quality and various texture related metrics.Comment: Error in table 1 correcte