20 research outputs found

    Learning Representations for Controllable Image Restoration

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    Deep Convolutional Neural Networks have sparked a renaissance in all the sub-fields of computer vision. Tremendous progress has been made in the area of image restoration. The research community has pushed the boundaries of image deblurring, super-resolution, and denoising. However, given a distorted image, most existing methods typically produce a single restored output. The tasks mentioned above are inherently ill-posed, leading to an infinite number of plausible solutions. This thesis focuses on designing image restoration techniques capable of producing multiple restored results and granting users more control over the restoration process. Towards this goal, we demonstrate how one could leverage the power of unsupervised representation learning. Image restoration is vital when applied to distorted images of human faces due to their social significance. Generative Adversarial Networks enable an unprecedented level of generated facial details combined with smooth latent space. We leverage the power of GANs towards the goal of learning controllable neural face representations. We demonstrate how to learn an inverse mapping from image space to these latent representations, tuning these representations towards a specific task, and finally manipulating latent codes in these spaces. For example, we show how GANs and their inverse mappings enable the restoration and editing of faces in the context of extreme face super-resolution and the generation of novel view sharp videos from a single motion-blurred image of a face. This thesis also addresses more general blind super-resolution, denoising, and scratch removal problems, where blur kernels and noise levels are unknown. We resort to contrastive representation learning and first learn the latent space of degradations. We demonstrate that the learned representation allows inference of ground-truth degradation parameters and can guide the restoration process. Moreover, it enables control over the amount of deblurring and denoising in the restoration via manipulation of latent degradation features

    Rapport annuel 2011-2012

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    Rapport annuel 2013

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    Video post processing architectures

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    NASA Tech Briefs, April 2000

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    Topics covered include: Imaging/Video/Display Technology; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Bio-Medical; Test and Measurement; Mathematics and Information Sciences; Books and Reports

    Video-based face recognition using multiple face orientations

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    This work is focused on designing and implementing a real-time video-based face identification system with low memory and computational requirements and high recognition rates. Since pro le features are stronger and, therefore, better when characterising faces than frontal faces, the system will detect and identify not only pure frontal but also pro le faces. This property of pro le faces will help to improve face recognition rates depending on the strategy for fusion of results used. Also, dimensionality reduction techniques will be studied and tested in order to nd the fastest and most efective one. Modi cation in k Nearest Neighbor classi er will be carried out to add a penalisation factor in function of the distance, increasing classi cation results and strictness. In order to nd which are the best options for reducing computational requirements in a face identi cation system several simulations will be performed. Among many others, simulations will look for optimal values of the k parameter in k Nearest Neighbor, the number of transformed coe cients kept in a feature vector or the minimum size of face images and will test dimensionality reduction in images, variation of the number of models or fusion of results. Finally, this work will show how a real-time system can be implemented in an ordinary computer obtaining successful results whether it be in real-time, adverse or controlled conditions environments

    Rapport annuel 2007-2008

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