Models, Bayesian estimation and
Keywords: image, modeling, estimation, deconvolution,
Bayesian, Markovian, adaptive, regularization, multiscale, remote
sensing, satellite, aerial.
Satellite or aerial images are corrupted by the optical system and the sensor. To reconstruct a good quality image from a noisy and blurred observation, one needs to perform a deconvolution.
First, we recall the principles of the acquisition chain, from optics to the sensor (visible or infrared), enabling us to model the degradation of the image.
In order to reconstruct the image without amplifying the noise, while preserving edges and textures, it is necessary to impose constraints on the reconstructed solution, which consists of choosing a prior model. We study satellite and aerial image modeling, which can be done within both probabilistic and variational frameworks, and using both discrete and continuous models. We propose new statistical models that take into account the fractal properties of natural scenes and their non-stationarity, using multiscale and adaptive approaches.
Next we study different techniques for estimating the model parameters, describing the properties of the images to be reconstructed. These techniques are developed within a Bayesian framework, and can be solved using either stochastic, or deterministic algorithms, depending on the problem.
Finally, we propose new fully automatic reconstruction algorithms. First, we suppose that the degradations (blurring kernel and noise statistics) are known, and we try to reconstruct the unknown image. Second, we consider the case where these degradations are unknown. We perform a blind deconvolution, in two steps, the first step consisting of determining the instrumental parameters, and the second of deconvolving the image with fixed degradation parameters.
Tests have been performed on remote sensing data such as satellite images (SPOT 5 and Pléïades simulations) and high resolution visible and infrared aerial images.