A. Pizurica

A Bayesian approach to nonlinear diffusion based on a Laplacian prior for ideal image gradient

We study the relationships between diffusivity functions in a nonlinear diffusion scheme and probabilities of edge presence under a marginal prior on ideal, noise-free image gradient. In particular we impose a Laplacian-shaped prior for the ideal gradient and we define the diffusivity function explicitly in terms of edge probabilities under this prior. The resulting diffusivity function has no free parameters to optimize. Our results demonstrate that the new diffusivity function, automatically, i.e., without any parameter adjustments, satisfies the well accepted criteria for the goodness of edge-stopping functions. Our results also offer a new and interesting interpretation of some widely used diffusivity functions, which are now compared to edge-stopping functions under a marginal prior for the ideal image gradient

Quantitative microwave tomography from sparse measurements using a robust huber regularizer

In statistical theory, the Huber function yields robust estimations reducing the effect of outliers. In this paper, we employ the Huber function as regularization in a challenging inverse problem: quantitative microwave imaging. Quantitative microwave tomography aims at estimating the permittivity profile of a scattering object based on measured scattered fields, which is a nonlinear, ill-posed inverse problem. The results on 3D data sets are encouraging: the reconstruction error is reduced and the permittivity profile can be estimated from fewer measurements compared to state-of-the art inversion procedures.

A Distributed Coding-Based Content-Aware Multi-View Video System

Compared to traditional mono-view systems, stereo or in general multi-view systems provide interesting additional information about a captured scene, which can significantly facilitate content extraction. This property makes them very useful for many emerging applications, such as 3D TV and video surveillance. However, the use of such systems has been limited so far because of the processing time and bandwidth requirements for multi-view data. These major drawbacks can only be relieved by the development of dedicated algorithms. In this paper, we present an efficient, flexible and content-aware coding method for a multi-view video system. The framework consists of a central processor and camera, completed by a flexible number of smart Wyner-Ziv cameras. The latter ones provide a content-aware representation of their viewpoint, thus greatly reducing the amount of data to be sent to the central processor. By employing Distributed Video (DV) coding, i.e. joint decoding of the independently encoded frames of the different cameras, we achieve good coding efficiency without inter-camera communication.

A new restoration method and its application to speckle images

The visual interpretation of ultrasound brain images is a proven method to detect the white matter damage at an early stage. A problem, common to all medical ultrasound images, is the presence of speckle noise, which not only complicates the visual interpretation of images, but also quantitative measurements. This paper proposes a new filter that removes a significant amount of speckle noise from ultrasound images, while preserving details very well. The filter is based on a new cleaning technique that operates on the detail images of a wavelet decomposition. The paper illustrates that the proposed technique has some advantages over other popular techniques, i.e., the ones proposed by Lee (1980), Frost (1982), Malfait and Roose (1997), and Sattar et al. (1997).

Combined non-local and multi-resolution sparsity prior in image restoration

In the field of image denoising, the non-local means (NLMS) filter is a conceptually simple, yet powerful technique. This filter exploits non-local, i.e. spatially repetitive, structure in natural images to estimate noise-free structure. In contrast, a wide variety of image restoration problems have been solved exploiting local smoothness of natural images, e.g. by enforcing sparsity of images when subjected to a multi-resolution transform. In this paper we introduce the prior knowledge of non-local repetitiveness of image structures into a broad multi-resolution image restoration framework. The proposed framework allows the power of the NLMS filter, supplemented by multi-resolution sparsity, to be extended for a wide variety of image restoration problems, such as demosaicing, deconvolution, reconstruction from insufficient measurements,... in a conceptually simple way.

Detecting variable source areas from temporal radar imagery using advanced image enhancement techniques

Recently, N. E. C. Verhoest et al. (1998) showed that it is possible to map variable source areas in a catchment using a principal component analysis. This technique, based on a temporal series of images, revealed the spatial soil moisture patterns from the vegetation and topographic effects introduced in a synthetic aperture radar (SAR) image. However, the obtained image is still corrupted with noise, which is partially related to the speckle observed within a SAR image. In order to get a noiseless image, which is more appropriate for hydrological modelling schemes, the authors apply a recently developed wavelet-based image denoising technique, A. Pizurica et al. (1999). The main advantage of this filtering technique is that it preserves the spatial patterns and observed edges, while it increases the signal to noise ratio significantly. The suitability of this denoising algorithm is investigated by comparing the hydrologic information included in these visually well-appearing images with the results obtained for their non-filtered counterparts.

Classification of multi-source images using color morphological profiles

In the remote sensing domain data from many different sources are often available. Each of these data sources are characterized by their own sensor- and platform-specific properties, i.e. spectral range, or spatial and spectral resolution. In this paper we consider a low spatial, but high spectral resolution satellite image, together with its high spatial resolution RGB color image, e.g. obtained by UAV. Spatial features are extracted from the color image by combining the three color bands R, G and B, ordering these color vectors, and presenting color mathematical morphological profiles accordingly. This way the spatial information contained in the correlation between the different bands is completely taken into account and thus also totally preserved in the feature extraction. In a classification experiment these color morphological profiles are combined with the spectral features of the hyperspectral image, and we show that the spatial characterization of the color image is improved.