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.

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.