Enhancing Flood Forecasting with Dual State-Parameter Estimation and Ensemble-based SAR Data Assimilation

Hydrodynamic

Étude des artefacts de flou, ringing et aliasing en imagerie numérique : application à la restauration

Introduction en français, corps du texte en anglaisThis thesis deais with the problems related to the formation of numerical images. Sampling, which is a necessary stage for the formation of a discrete image from a continuous one, may introduce some artifacts that degrade the image quality. These artifacts are called blur, ringing and aliasing. The main motivation in this thesis was the study of these three artifacts. In the first part, we recall the image formation process and we define these artifacts. In the second part, we propose a new measure of both the ringing and blur artifacts associated to a low pass filtering prior to sampling. The third part is dedicated to the automatic detection of these artifacts in images. In the fourth part, the automatic detection is tested on two real restoration applications: the blind deconvolution and the denoising.Cette thèse aborde les problèmes liés à la formation des images numériques. L'étape d'échantillonnage nécessaire à la formation d'une image discrète à partir d'une image continue peut introduire différents types d'artefacts qui constituent des dégradations majeures de la qualité de l'image. La motivation principale de cette thèse a été l'étude de ces artefacts que sont le flou, le ringing et l'aliasing. Dans la première partie, nous rappelons tout d'abord le processus de formation des images numériques puis nous proposons des définitions de ces artefacts. Dans la deuxième partie, nous définissons une mesure conjointe du flou et du ringing dans le cadre d'un filtrage passe bas précédant l'échantillonnage. La troisième partie est dédiée à la détection automatique de ces artefacts dans les images. Enfin, en quatrième partie, la détection automatique est testée dans des applications concrètes de la restauration d'images: la déconvolution aveugle et le débruitage

Measuring the Global Phase Coherence of an image

International audienceThe Fourier phase spectrum of an image is well known to contain crucial information about the image geometry, in particular its contours. In this paper, we show that it is also strongly related to the image quality, in particular its sharpness. We propose a way to define the Global Phase Coherence (GPC) of an image, by estimating the volume of all possible phase functions that, associated with the measured modulus, produce images that are not less likely than the original image. The likelihood is measured with the total variation (Rudin-Osher-Fatemi implicit prior), and the numerical estimation is realized by a Monte-Carlo simulation. We show that the obtained GPC measure decreases with blur, noise, and ringing, and thus provides a new interesting sharpness indicator, that can be used for parametric blind deconvolution, as demonstrated by experiments

Study of the blur, ringing and aliasing artifacts in numerical imaging (application to restoration)

Cette thèse aborde les problèmes liés à la formation des images numériques. L'étape d'échantillonnage nécessaire à la formation d'une image discrète à partir d'une image continue peut introduire différents types d'artefacts qui constituent des dégradations majeures de la qualité de l'image. La motivation principale de cette thèse a été l'étude de ces artefacts que sont le flou, le ringing et l'aliasing. Dans la première partie, nous rappelons tout d'abord le processus de formation des images numériques puis nous proposons des définitions de ces artefacts. Dans la deuxième partie, nous définissons une mesure conjointe du flou et du ringing dans le cadre d'un filtrage passe bas précédant l'échantillonnage. La troisième partie est dédiée à la détection automatique de ces artefacts dans les images. Enfin, en quatrième partie, la détection automatique est testée dans des applications concrètes de la restauration d'images: la déconvolution aveugle et le débruitageThis thesis deais with the problems related to the formation of numerical images. Sampling, which is a necessary stage for the formation of a discrete image from a continuous one, may introduce some artifacts that degrade the image quality. These artifacts are called blur, ringing and aliasing. The main motivation in this thesis was the study of these three artifacts. In the first part, we recall the image formation process and we define these artifacts. In the second part, we propose a new measure of both the ringing and blur artifacts associated to a low pass filtering prior to sampling. The third part is dedicated to the automatic detection of these artifacts in images. In the fourth part, the automatic detection is tested on two real restoration applications: the blind deconvolution and the denoising.CACHAN-ENS (940162301) / SudocSudocFranceF

Automatic detection of well sampled images via a new ringing measure

According to Shannon Sampling Theory, Fourier interpolation is the optimal way to reach subpixel accuracy from a properly-sampled digital image. However, for most images this interpolation tends to produce an artifact called ringing, that consists in undesirable oscillations near objects contours. In this work, we propose a way to detect this ringing artifact. Using Euler zigzag numbers, we compute the probability that neighboring gray-levels form an alternating sequence by chance, and characterize these undesirable ringing blocks as structures that would be very unlikely in a random image. We then show two applications where the associated algorithm is used to test or enforce the compliance of an image with Fourier interpolation. Index Terms — Image sampling, ringing, Fourier interpolation

Proxy Data of Surface Water Floods in Rural Areas: Application to the Evaluation of the IRIP Intense Runoff Mapping Method Based on Satellite Remote Sensing and Rainfall Radar

Along with fluvial floods (FFs), surface water floods (SWFs) caused by extreme overland flow are one of the main flood hazards occurring after heavy rainfall. Using physics-based distributed hydrological models, surface runoff can be simulated from precipitation inputs to investigate regions prone to soil erosion, mudflows or landslides. Geomatics approaches have also been developed to map susceptibility towards intense surface runoff without explicit hydrological modeling or event-based rainfall forcing. However, in order for these methods to be applicable for prevention purposes, they need to be comprehensively evaluated using proxy data of runoff-related impacts following a given event. Here, the IRIP geomatics mapping model, or “Indicator of Intense Pluvial Runoff”, is faced with rainfall radar measurements and damage maps derived from satellite imagery and supervised classification algorithms. Six watersheds in the Aude and Alpes-Maritimes departments in the South of France are investigated over more than 2000 km2 of rural areas during two flash-flood events. The results of this study show that the greater the IRIP susceptibility scores, the more SWFs are detected by the remote sensing-based detection algorithm. Proportions of damaged plots become even larger when considering areas which experienced heavier precipitations. A negative relationship between the mean IRIP accumulation scores and the intensity of rainfall is found among damaged plots, confirming that SWFs preferably occur over potentially riskier areas where rainfall is lower. Land use and soil hydraulic conductivity are identified as the most relevant indicators for IRIP to define production areas responsible for downslope deteriorations. Multivariate logistic regression is also used to determine the relative weights of upstream and local topography, uphill production areas and rainfall intensity for explaining SWF occurrence. This work overall confirms the relevance of IRIP methodology while suggesting improvements to its core framework to implement better prevention strategies against SWF-related hazards.</jats:p