Inférence bayésienne dans des problèmes inverses, myopes et aveugles en traitement du signal et des images

Les activités de recherche présentées concernent la résolution de problèmes inverses, myopes et aveugles rencontrés en traitement du signal et des images. Les méthodes de résolution privilégiées reposent sur une démarche d'inférence bayésienne. Celle-ci offre un cadre d'étude générique pour régulariser les problèmes généralement mal posés en exploitant les contraintes inhérentes aux modèles d'observation. L'estimation des paramètres d'intérêt est menée à l'aide d'algorithmes de Monte Carlo qui permettent d'explorer l'espace des solutions admissibles. Un des domaines d'application visé par ces travaux est l'imagerie hyperspectrale et, plus spécifiquement, le démélange spectral. Le second travail présenté concerne la reconstruction d'images parcimonieuses acquises par un microscope MRFM

New approaches for unsupervised transcriptomic data analysis based on Dictionary learning

The era of high-throughput data generation enables new access to biomolecular profiles and exploitation thereof. However, the analysis of such biomolecular data, for example, transcriptomic data, suffers from the so-called "curse of dimensionality". This occurs in the analysis of datasets with a significantly larger number of variables than data points. As a consequence, overfitting and unintentional learning of process-independent patterns can appear. This can lead to insignificant results in the application. A common way of counteracting this problem is the application of dimension reduction methods and subsequent analysis of the resulting low-dimensional representation that has a smaller number of variables. In this thesis, two new methods for the analysis of transcriptomic datasets are introduced and evaluated. Our methods are based on the concepts of Dictionary learning, which is an unsupervised dimension reduction approach. Unlike many dimension reduction approaches that are widely applied for transcriptomic data analysis, Dictionary learning does not impose constraints on the components that are to be derived. This allows for great flexibility when adjusting the representation to the data. Further, Dictionary learning belongs to the class of sparse methods. The result of sparse methods is a model with few non-zero coefficients, which is often preferred for its simplicity and ease of interpretation. Sparse methods exploit the fact that the analysed datasets are highly structured. Indeed, a characteristic of transcriptomic data is particularly their structuredness, which appears due to the connection of genes and pathways, for example. Nonetheless, the application of Dictionary learning in medical data analysis is mainly restricted to image analysis. Another advantage of Dictionary learning is that it is an interpretable approach. Interpretability is a necessity in biomolecular data analysis to gain a holistic understanding of the investigated processes. Our two new transcriptomic data analysis methods are each designed for one main task: (1) identification of subgroups for samples from mixed populations, and (2) temporal ordering of samples from dynamic datasets, also referred to as "pseudotime estimation". Both methods are evaluated on simulated and real-world data and compared to other methods that are widely applied in transcriptomic data analysis. Our methods convince through high performance and overall outperform the comparison methods

From active to passive spatial acoustic sensing and applications

The active acoustic sensing system emits modulated acoustic waves and analyzes reflection signals. It is dominant in acoustic spatial sensing. On the other side, the passive acoustic sensing system receives and investigates nature sounds directly. It is good at semantic tasks but has weak performance on spatial sensing. In this dissertation, we manage to bridge three gaps in existing systems. They are the gap between the assumption of signal processing algorithms and the real acoustic environment, the gap between powerful active spatial sensing and limited passive spatial sensing, and the gap between the semantic features and spatial information. We evolve the acoustic sensing system design and extend the functionalities by three novel systems. First, we develop a fully active spatial sensing system DeepRange which can adapt to the real environment easily. We develop an effective mechanism to generate synthetic training data that captures noise, speaker/mic distortion, and interference in the signals. It removes the need of collecting a large volume of data. We then design a deep range neural network (DRNet) to estimate the distance from raw acoustic signals. It is inspired by signal processing that an ultra-long convolution kernel size helps to combat noise and interference. The model is fully trained over synthetic data, but it can achieve sub-centimeter error robustly in real data despite various environments, background noise, interference, and mobile phone models. Second, we develop a fused active and passive spatial sensing system for speech separation noted as Spatial Aware Multi-task learning-based Separation (SAMS). We leverage both active sensing and passive sensing to improve AoA estimation and jointly optimize the semantic task and the spatial task. SAMS estimates the spatial location and extracts speech for the target user during teleconferencing simultaneously. We first generate fine-grained spatial embeddings from the user’s voice and inaudible tracking sound, which contains the user’s position and rich multipath information. Furthermore, we develop a deep neural network with multi-task learning to jointly optimize source separation and location. We significantly speed up inference to provide a real-time guarantee. Finally, we deeply fuse the semantic features and spatial cues to combat the interference and noise in the real environment as well as enable depth sensing in a fully passive setup. Inspired by the ”flash-to-bang” phenomenon (i.e.hearing the thunder after seeing the lightning), we propose FBDepth to measure the depth of the sound source. We formulate the problem as an audio-visual event localization task for collision events. Specifically, FBDepth first aligns correspondence between the video track and audio track to locate the target object and target sound in a coarse granularity. Based on the observation of moving objects’ trajectories, it proposes to estimate the intersection of optical flow before and after the collision to locate video events in time. It feeds the estimated timestamp of the video event and the other modalities for the final depth estimation. We use a mobile phone to collect the 3.6K+ video clips involving 24 different objects at up to 60m. FBDepth shows superior performance especially at a long range compared to monocular and stereo methods.Computer Science

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Ultrasound-coupled semi-supervised nonnegative matrix factorisation for speech enhancement

We present an extension to an existing speech enhancement tech-nique, whereby the incorporation of easily obtained Doppler-based ultrasound data, obtained from frequency shifts caused by a talker’s mouth movements, is shown to improve speech enhancement re-sults. Noisy speech mixtures were enhanced using semi-supervised nonnegative matrix factorisation (NMF). Ultrasound data recorded alongside the speech is transformed into the spectral domain and used additionally to audio in the mixture to be separated. Speech components are learned from a training set, whilst noise compo-nents are estimated from the mixture signal. We show that the ultra-sound data can improve source-to-distortion ratios for the enhanced speech, relative to both the non-ultrasound NMF case and an estab-lished Wiener filter-based speech enhancement method

Blind image deconvolution: nonstationary Bayesian approaches to restoring blurred photos

High quality digital images have become pervasive in modern scientific and everyday life — in areas from photography to astronomy, CCTV, microscopy, and medical imaging. However there are always limits to the quality of these images due to uncertainty and imprecision in the measurement systems. Modern signal processing methods offer the promise of overcoming some of these problems by postprocessing these blurred and noisy images. In this thesis, novel methods using nonstationary statistical models are developed for the removal of blurs from out of focus and other types of degraded photographic images. The work tackles the fundamental problem blind image deconvolution (BID); its goal is to restore a sharp image from a blurred observation when the blur itself is completely unknown. This is a “doubly illposed” problem — extreme lack of information must be countered by strong prior constraints about sensible types of solution. In this work, the hierarchical Bayesian methodology is used as a robust and versatile framework to impart the required prior knowledge. The thesis is arranged in two parts. In the first part, the BID problem is reviewed, along with techniques and models for its solution. Observation models are developed, with an emphasis on photographic restoration, concluding with a discussion of how these are reduced to the common linear spatially-invariant (LSI) convolutional model. Classical methods for the solution of illposed problems are summarised to provide a foundation for the main theoretical ideas that will be used under the Bayesian framework. This is followed by an indepth review and discussion of the various prior image and blur models appearing in the literature, and then their applications to solving the problem with both Bayesian and nonBayesian techniques. The second part covers novel restoration methods, making use of the theory presented in Part I. Firstly, two new nonstationary image models are presented. The first models local variance in the image, and the second extends this with locally adaptive noncausal autoregressive (AR) texture estimation and local mean components. These models allow for recovery of image details including edges and texture, whilst preserving smooth regions. Most existing methods do not model the boundary conditions correctly for deblurring of natural photographs, and a Chapter is devoted to exploring Bayesian solutions to this topic. Due to the complexity of the models used and the problem itself, there are many challenges which must be overcome for tractable inference. Using the new models, three different inference strategies are investigated: firstly using the Bayesian maximum marginalised a posteriori (MMAP) method with deterministic optimisation; proceeding with the stochastic methods of variational Bayesian (VB) distribution approximation, and simulation of the posterior distribution using the Gibbs sampler. Of these, we find the Gibbs sampler to be the most effective way to deal with a variety of different types of unknown blurs. Along the way, details are given of the numerical strategies developed to give accurate results and to accelerate performance. Finally, the thesis demonstrates state of the art results in blind restoration of synthetic and real degraded images, such as recovering details in out of focus photographs

Proceedings of the 35th International Workshop on Statistical Modelling : July 20- 24, 2020 Bilbao, Basque Country, Spain

466 p.The InternationalWorkshop on Statistical Modelling (IWSM) is a reference workshop in promoting statistical modelling, applications of Statistics for researchers, academics and industrialist in a broad sense. Unfortunately, the global COVID-19 pandemic has not allowed holding the 35th edition of the IWSM in Bilbao in July 2020. Despite the situation and following the spirit of the Workshop and the Statistical Modelling Society, we are delighted to bring you the proceedings book of extended abstracts

Proceedings of the 35th International Workshop on Statistical Modelling : July 20- 24, 2020 Bilbao, Basque Country, Spain

466 p.The InternationalWorkshop on Statistical Modelling (IWSM) is a reference workshop in promoting statistical modelling, applications of Statistics for researchers, academics and industrialist in a broad sense. Unfortunately, the global COVID-19 pandemic has not allowed holding the 35th edition of the IWSM in Bilbao in July 2020. Despite the situation and following the spirit of the Workshop and the Statistical Modelling Society, we are delighted to bring you the proceedings book of extended abstracts