Time warping between main epidemic time series in epidemiological surveillance.

The most common reported epidemic time series in epidemiological surveillance are the daily or weekly incidence of new cases, the hospital admission count, the ICU admission count, and the death toll, which played such a prominent role in the struggle to monitor the Covid-19 pandemic. We show that pairs of such curves are related to each other by a generalized renewal equation depending on a smooth time varying delay and a smooth ratio generalizing the reproduction number. Such a functional relation is also explored for pairs of simultaneous curves measuring the same indicator in two neighboring countries.

Learning from the past: A short term forecast method for the COVID-19 incidence curve.

The COVID-19 pandemy has created a radically new situation where most countries provide raw measurements of their daily incidence and disclose them in real time. This enables new machine learning forecast strategies where the prediction might no longer be based just on the past values of the current incidence curve, but could take advantage of observations in many countries. We present such a simple global machine learning procedure using all past daily incidence trend curves.

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Imagery.

Methane (CH) emission estimates from top-down studies over oil and gas basins have revealed systematic underestimation of CH emissions in current national inventories. Sparse but extremely large amounts of CH from oil and gas production activities have been detected across the globe, resulting in a significant increase of the overall oil and gas contribution. However, attribution to specific facilities remains a major challenge unless high-spatial-resolution images provide sufficient granularity within the oil and gas basin.

Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise.

The sanitary crisis of the past two years has focused the public's attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number Rt, defined by the average number of new infections caused by a single infected individual at time , is one of the best metrics for estimating the epidemic trend. In this paper, we provide a complete observation model for sampled epidemiological incidence signals obtained through periodic administrative measurements.

Computing the daily reproduction number of COVID-19 by inverting the renewal equation using a variational technique.

The COVID-19 pandemic has undergone frequent and rapid changes in its local and global infection rates, driven by governmental measures or the emergence of new viral variants. The reproduction number indicates the average number of cases generated by an infected person at time and is a key indicator of the spread of an epidemic. A timely estimation of is a crucial tool to enable governmental organizations to adapt quickly to these changes and assess the consequences of their policies.

Forgery Detection in Digital Images by Multi-Scale Noise Estimation.

A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing.

Full-spectrum denoising of high-SNR hyperspectral images.

The high spectral redundancy of hyper/ultraspectral Earth-observation satellite imaging raises three challenges: (a) to design accurate noise estimation methods, (b) to denoise images with very high signal-to-noise ratio (SNR), and (c) to secure unbiased denoising. We solve (a) by a new noise estimation, (b) by a novel Bayesian algorithm exploiting spectral redundancy and spectral clustering, and (c) by accurate measurements of the interchannel correlation after denoising. We demonstrate the effectiveness of our method on two ultraspectral Earth imagers, IASI and IASI-NG, one flying and the other in project, and sketch the major resolution gain of future instruments entailed by such unbiased denoising.

A Precision Analysis of Camera Distortion Models.

This paper addresses the question of identifying the right camera direct or inverse distortion model, permitting a high subpixel precision to fit to real camera distortion. Five classic camera distortion models are reviewed and their precision is compared for direct or inverse distortion. By definition, the three radially symmetric models can only model a distortion radially symmetric around some distortion center.

Improving wavefront sensing with a Shack-Hartmann device.

To achieve higher resolutions, current earth observation satellites use larger, lightweight primary mirrors that can deform over time, affecting the image quality. To overcome this problem, we evaluated the possibility of combining a deformable mirror with a Shack-Hartman wavefront sensor (SHWFS) directly in the satellite. The SHWFS's performance depends entirely on the accuracy of the shift estimation algorithm employed, which should be computationally cheap to execute onboard.

Good continuation in dot patterns: A quantitative approach based on local symmetry and non-accidentalness.

We propose a novel approach to the grouping of dot patterns by the good continuation law. Our model is based on local symmetries, and the non-accidentalness principle to determine perceptually relevant configurations. A quantitative measure of non-accidentalness is proposed, showing a good correlation with the visibility of a curve of dots.

A Contrario 2D Point Alignment Detection.

In spite of many interesting attempts, the problem of automatically finding alignments in a 2D set of points seems to be still open. The difficulty of the problem is illustrated here by very simple examples. We then propose an elaborate solution.

Nonparametric multiscale blind estimation of intensity-frequency-dependent noise.

The camera calibration parameters and the image processing chain which generated a given image are generally not available to the receiver. This happens for example with scanned photographs and for most JPEG images. These images have undergone various nonlinear contrast changes and also linear and nonlinear filters.

Multiscale image blind denoising.

Arguably several thousands papers are dedicated to image denoising. Most papers assume a fixed noise model, mainly white Gaussian or Poissonian. This assumption is only valid for raw images.

Nonparametric noise estimation method for raw images.

Optimal denoising works at best on raw images (the image formed at the output of the focal plane, at the CCD or CMOS detector), which display a white signal-dependent noise. The noise model of the raw image is characterized by a function that given the intensity of a pixel in the noisy image returns the corresponding standard deviation; the plot of this function is the noise curve. This paper develops a nonparametric approach estimating the noise curve directly from a single raw image.

High-precision camera distortion measurements with a "calibration harp".

This paper addresses the high-precision measurement of the distortion of a digital camera from photographs. Traditionally, this distortion is measured from photographs of a flat pattern that contains aligned elements. Nevertheless, it is nearly impossible to fabricate a very flat pattern and to validate its flatness.

Meaningful matches in stereovision.

This paper introduces a statistical method to decide whether two blocks in a pair of images match reliably. The method ensures that the selected block matches are unlikely to have occurred "just by chance." The new approach is based on the definition of a simple but faithful statistical background model for image blocks learned from the image itself.

The collaboration of grouping laws in vision.

Gestalt theory gives a list of geometric grouping laws that could in principle give a complete account of human image perception. Based on an extensive thesaurus of clever graphical images, this theory discusses how grouping laws collaborate, and conflict toward a global image understanding. Unfortunately, as shown in the bibliographical analysis herewith, the attempts to formalize the grouping laws in computer vision and psychophysics have at best succeeded to compute individual partial structures (or partial gestalts), such as alignments or symmetries.

Dimensionality of color space in natural images.

The color histogram (or color cloud) of a digital image displays the colors present in an image regardless of their spatial location and can be visualized in (R,G,B) coordinates. Therefore, it contains essential information about the structure of colors in natural scenes. The analysis and visual exploration of this structure is difficult.

Random phase textures: theory and synthesis.

This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise (RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They arguably derive from linearized versions of two early Julesz texture discrimination theories.

A PDE formalization of Retinex theory.

In 1964 Edwin H. Land formulated the Retinex theory, the first attempt to simulate and explain how the human visual system perceives color. His theory and an extension, the "reset Retinex" were further formalized by Land and McCann.

Fast cartoon + texture image filters.

Can images be decomposed into the sum of a geometric part and a textural part? In a theoretical breakthrough, [Y. Meyer, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations. Providence, RI: American Mathematical Society, 2001] proposed variational models that force the geometric part into the space of functions with bounded variation, and the textural part into a space of oscillatory distributions.

LSD: a fast line segment detector with a false detection control.

We propose a linear-time line segment detector that gives accurate results, a controlled number of false detections, and requires no parameter tuning. This algorithm is tested and compared to state-of-the-art algorithms on a wide set of natural images.

Self-similarity driven color demosaicking.

Demosaicking is the process by which from a matrix of colored pixels measuring only one color component per pixel, red, green, or blue, one can infer a whole color information at each pixel. This inference requires a deep understanding of the interaction between colors, and the involvement of image local geometry. Although quite successful in making such inferences with very small relative error, state-of-the-art demosaicking methods fail when the local geometry cannot be inferred from the neighboring pixels.

Dequantizing image orientation.

We address the problem of computing a local orientation map in a digital image. We show that standard image gray level quantization causes a strong bias in the repartition of orientations, hindering any accurate geometric analysis of the image. In continuation, a simple dequantization algorithm is proposed, which maintains all of the image information and transforms the quantization noise in a nearby Gaussian white noise (we actually prove that only Gaussian noise can maintain isotropy of orientations).