Real-time multiframe blind deconvolution of solar images

The quality of images of the Sun obtained from the ground are severely limited by the perturbing effect of the turbulent Earth's atmosphere. The post-facto correction of the images to compensate for the presence of the atmosphere require the combination of high-order adaptive optics techniques, fast measurements to freeze the turbulent atmosphere and very time consuming blind deconvolution algorithms. Under mild seeing conditions, blind deconvolution algorithms can produce images of astonishing quality. They can be very competitive with those obtained from space, with the huge advantage of the flexibility of the instrumentation thanks to the direct access to the telescope. In this contribution we leverage deep learning techniques to significantly accelerate the blind deconvolution process and produce corrected images at a peak rate of ~100 images per second. We present two different architectures that produce excellent image corrections with noise suppression while maintaining the photometric properties of the images. As a consequence, polarimetric signals can be obtained with standard polarimetric modulation without any significant artifact. With the expected improvements in computer hardware and algorithms, we anticipate that on-site real-time correction of solar images will be possible in the near future.Comment: 16 pages, 12 figures, accepted for publication in A&

Invariant Scattering Transform for Medical Imaging

Invariant scattering transform introduces new area of research that merges the signal processing with deep learning for computer vision. Nowadays, Deep Learning algorithms are able to solve a variety of problems in medical sector. Medical images are used to detect diseases brain cancer or tumor, Alzheimer's disease, breast cancer, Parkinson's disease and many others. During pandemic back in 2020, machine learning and deep learning has played a critical role to detect COVID-19 which included mutation analysis, prediction, diagnosis and decision making. Medical images like X-ray, MRI known as magnetic resonance imaging, CT scans are used for detecting diseases. There is another method in deep learning for medical imaging which is scattering transform. It builds useful signal representation for image classification. It is a wavelet technique; which is impactful for medical image classification problems. This research article discusses scattering transform as the efficient system for medical image analysis where it's figured by scattering the signal information implemented in a deep convolutional network. A step by step case study is manifested at this research work.Comment: 11 pages, 8 figures and 1 tabl

Augmented breast tumor classification by perfusion analysis

Magnetic resonance and computed tomography imaging aid in the diagnosis and analysis of pathologic conditions. Blood flow, or perfusion, through a region of tissue can be computed from a time series of contrast-enhanced images. Perfusion is an important set of physiological parameters that reflect angiogenesis. In cancer, heightened angiogenesis is a key process in the growth and spread of tumorous masses. An automatic classification technique using recovered perfusion may prove to be a highly accurate diagnostic tool. Such a classification system would supplement existing histopathological tests, and help physicians to choose the most optimal treatment protocol. Perfusion is obtained through deconvolution of signal intensity series and a pharmacokinetic model. However, many computational problems complicate the accurate-consistent recovery of perfusion. The high time-resolution acquisition of images decreases signal-to-noise, producing poor deconvolution solutions. The delivery of contrast agent as a function of time must also be determined or sampled before deconvolution can proceed. Some regions of the body, such as the brain, provide a nearby artery to serve as this arterial input function. Poor estimates can lead to an over or under estimation of perfusion. Breast tissue is an example of one tissue region where a clearly defined artery is not present. This proposes a new method of using recovered perfusion and spatial information in an automated classifier. This classifier grades suspected lesions as benign or malignant. This method can be integrated into a computer-aided diagnostic system to enhance the value of medical imagery

Video event detection and visual data pro cessing for multimedia applications

Cette thèse (i) décrit une procédure automatique pour estimer la condition d'arrêt des méthodes de déconvolution itératives basées sur un critère d'orthogonalité du signal estimé et de son gradient à une itération donnée; (ii) présente une méthode qui décompose l'image en une partie géométrique (ou "cartoon") et une partie "texture" en utilisation une estimation de paramètre et une condition d'arrêt basées sur la diffusion anisotropique avec orthogonalité, en utilisant le fait que ces deux composantes. "cartoon" et "texture", doivent être indépendantes; (iii) décrit une méthode pour extraire d'une séquence vidéo obtenue à partir de caméra portable les objets de premier plan en mouvement. Cette méthode augmente la compensation de mouvement de la caméra par une nouvelle estimation basée noyau de la fonction de probabilité de densité des pixels d'arrière-plan. Les méthodes présentées ont été testées et comparées aux algorithmes de l'état de l'art.This dissertation (i) describes an automatic procedure for estimating the stopping condition of non-regularized iterative deconvolution methods based on an orthogonality criterion of the estimated signal and its gradient at a given iteration; (ii) presents a decomposition method that splits the image into geometric (or cartoon) and texture parts using anisotropic diffusion with orthogonality based parameter estimation and stopping condition, utilizing the theory that the cartoon and the texture components of an image should be independent of each other; (iii) describes a method for moving foreground object extraction in sequences taken by wearable camera, with strong motion, where the camera motion compensated frame differencing is enhanced with a novel kernel-based estimation of the probability density function of the background pixels. The presented methods have been thoroughly tested and compared to other similar algorithms from the state-of-the-art.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Neuropathy Classification of Corneal Nerve Images Using Artificial Intelligence

Nerve variations in the human cornea have been associated with alterations in the neuropathy state of a patient suffering from chronic diseases. For some diseases, such as diabetes, detection of neuropathy prior to visible symptoms is important, whereas for others, such as multiple sclerosis, early prediction of disease worsening is crucial. As current methods fail to provide early diagnosis of neuropathy, in vivo corneal confocal microscopy enables very early insight into the nerve damage by illuminating and magnifying the human cornea. This non-invasive method captures a sequence of images from the corneal sub-basal nerve plexus. Current practices of manual nerve tracing and classification impede the advancement of medical research in this domain. Since corneal nerve analysis for neuropathy is in its initial stages, there is a dire need for process automation. To address this limitation, we seek to automate the two stages of this process: nerve segmentation and neuropathy classification of images. For nerve segmentation, we compare the performance of two existing solutions on multiple datasets to select the appropriate method and proceed to the classification stage. Consequently, we approach neuropathy classification of the images through artificial intelligence using Adaptive Neuro-Fuzzy Inference System, Support Vector Machines, Naïve Bayes and k-nearest neighbors. We further compare the performance of machine learning classifiers with deep learning. We ascertained that nerve segmentation using convolutional neural networks provided a significant improvement in sensitivity and false negative rate by at least 5% over the state-of-the-art software. For classification, ANFIS yielded the best classification accuracy of 93.7% compared to other classifiers. Furthermore, for this problem, machine learning approaches performed better in terms of classification accuracy than deep learning

Combining ICA Representations for Recognizing Faces

Independent Component Analysis (ICA) is a generalization of Principal Component Analysis (PCA), and it looks for components that are both statistically independent and non-Gaussian. ICA is sensitive to high-order statistic and it expected to outperform PCA in finding better basis images. Moreover, with face recognition, high-order relationships among pixels may have more important information than those of pairwise relationships on which base images found by PCA depend. Two different representations can be applied by ICA; ICA architecture I and ICA architecture II. A new classifier that combines the two ICA architectures is proposed for face recognition. By the new classifier, the similarity measure vector was employed in which the similarity measure vectors for both ICA representations were resorted in descending order and then integrated by merging the corresponding values of each vector. The new classifier was performed on face images in the AR Face Database. Cumulative Match Characteristic was taken as a measure for evaluating the performance of the new classifier with illumination variation, expression, and Occlusion. The proposed classifier outperforms both ICA architectures in all cases especially in later ranks

Superresolution fluorescence microscopy with structured illumination

The resolution of a conventional fluorescence microscope image is diffraction limited which achieves a spatial resolution of 200nm lateral and 500nm axial. Recently, many superresolution fluorescence microscopy techniques have been developed which allow the observation of many biological structures beyond the diffraction limit. Structured illumination microscopy (SIM) is one of them. The principle of SIM is based on using a harmonic light grid which down modulates the high spatial frequencies of the sample into the observable region of the microscope. The resolution enhancement is highly dependent on the reconstruction technique, which restores the high spatial frequencies of the sample to their original position. Common SIM reconstructions require the perfect knowledge of the illumination pattern. However, to perfectly control the harmonic illumination patterns on the sample plane is not easy in experimental implementations and this makes the experimental setup very technical. Reconstructing SIM images assuming the perfect knowledge of the illumination intensity patterns may, therefore, introduce artifacts on the estimated sample due to the misalignment of the grid that can occur during experimental acquisitions. To tackle this drawback of SIM, in this these, we have developed blind-SIM reconstruction strategies which are independent of the illumination patterns. Using the 3D blind-SIM reconstruction strategies we extended the harmonic SIM to speckle illumination microscopy which uses random unknown speckle patterns that need no control, unlike the harmonic grid patterns. For harmonic-SIM images, since incorporating some information about illumination patterns is valuable, we have developed a 3D positive filtered blind-SIM reconstruction which confines the iterative estimation of the illuminations in the vicinity of the Fourier peaks (using carefully designed Fourier filter masks) in the Fourier space. Using blind-SIM reconstruction techniques a lateral resolution of about 100nm and axial resolution of about 200nm is obtained in both speckle and harmonic SIM. In addition, to reduce the out-of-focus problem in widefield images, a simple computational technique which is based on reconstructing 2D data with 3D PSF is developed based on blind-SIM reconstruction. Moreover, to combine the functionalities of SIM and light sheet microscopy, as a proof of concept, we have developed a simple microscope setup which produces a structured light sheet illumination pattern.La microscopie de fluorescence optique est l’un des outils les plus puissants pour étudier les structures cellulaires et moléculaires au niveau subcellulaire. La résolution d’une image de microscope conventionnel à fluorescence est limitée par la diffraction, ce qui permet d’obtenir une résolution spatiale latérale de 200nm et axiale de 500nm. Récemment, de nombreuses techniques de microscopie de fluorescence de super-résolution ont été développées pour permettre d’observer de nombreuses structures biologiques au-delà de la limite de diffraction. La microscopie d’illumination structurée (SIM) est l’une de ces technologies. Le principe de la SIM est basé sur l’utilisation d’une grille de lumière harmonique qui permet de translater les hautes fréquences spatiales de l’échantillon vers la région d’observation du microscope. L’amélioration de la résolution de cette technologie de microscopie dépend fortement de la technique de reconstruction, qui rétablit les hautes fréquences spatiales de l’échantillon dans leur position d’origine. Les méthodes classiques de reconstruction SIM nécessitent une connaissance parfaite de l’illumination de l’échantillon. Cependant, l’implémentation d’un contrôle parfait de l’illumination harmonique sur le plan de l’échantillon n’est pas facile expérimentalement et il présente un grand défi. L’hypothèse de la connaissance parfaite de l’intensité de la lumière illuminant l’échantillon en SIM peut donc introduire des artefacts sur l’image reconstruite de l’échantillon, à cause des erreurs d’alignement de la grille qui peuvent se présenter lors de l’acquisition expérimentale. Afin de surmonter ce défi, nous avons développé dans cette thèse des stratégies de reconstruction «aveugle» qui sont indépendantes de d’illumination. À l’aide de ces stratégies de reconstruction dites «blind-SIM», nous avons étendu la SIM harmonique pour l’appliquer aux cas de «SIM-speckle» qui utilisent des illuminations aléatoires et inconnues qui contrairement à l’illumination harmonique, ne nécessitent pas de controle. Comme il est utile de récupérer des informations sur l’illumination en SIM harmonique, nous avons développé une reconstruction blind-SIM tridimensionnel et filtrée qui confine l’estimation itérative des illuminations au voisinage des pics dans l’espace de Fourier, en utilisant des masques de filtre de Fourier soigneusement conçus. En utilisant des techniques de reconstruction blind-SIM, une résolution latérale d’environ 100 nm et une résolution axiale d’environ 200 nm sont obtenues, à la fois en SIM harmonique et en SIM speckle. En outre, pour réduire le problème de focalisation dans les images de champ large, une technique de calcul simple qui repose sur la reconstruction bidimensionnel de données à partir de PSF tridimensionnel est développée. En outre, afin de combiner à la fois les fonctionnalités de la SIM et de la microscopie á nappe de lumière, en tant que preuve de concept, nous avons développé une configuration de microscope simple qui produit une nappe de lumière structuré