In this paper we propose a novel method for knowledge-based segmentation. Our contribution lies on the introduction of linear sub-spaces constraints within the random-walk segmentation framework. Prior knowledge is obtained through principal component analysis that is then combined with conventional boundary constraints for image segmentation. The approach is validated on a challenging clinical setting that is multicomponent segmentation of the human upper leg skeletal muscle in Magnetic Resonance Imaging, where there is limited visual differentiation support between muscle classes.

In this paper, we introduce a reconstruction framework that explicitly accounts for image geometry when defining the spatial interaction between pixels in the filtering process. To this end, image structure is captured using local co-occurrence statistics and is incorporated to the enhancement algorithm in a sequential fashion using the particle filtering technique. In this context, the reconstruction process is modeled using a dynamical system with multiple states and its evolution is guided by the prior density describing the image structure. Towards optimal exploration of the image geometry, an evaluation process of the state of the system is performed at each iteration. The resulting framework explores optimally spatial dependencies between image content towards variable bandwidth image reconstruction. Promising results using additive noise models demonstrate the potentials of such an explicit modeling of the geometry.

This paper introduces a variational formulation for image denoising based on a quadratic function over kernels of variable bandwidth. These kernels are scale adaptive and reflect spatial and photometric similarities between pixels. The bandwidth of the kernels is observation-dependent towards improving the accuracy of the reconstruction process and is constrained to be locally smooth. We analyze the evolution of the noise model form the RAW space to the RGB one, by propagating it over the image formation process. The experimental results demonstrate that the use of a variable bandwidth approach and an image intensity dependent noise variance ensures better restoration quality.

This paper introduces a new denoising technique that consists in recovering the image using a filtering function adapted to the image content. The definition of such a function relies on the computation of similarity between pixels of a given neighborhood. Our contribution consists in the definition of a new similarity criterion which is more robust to noise. This measure is computed from a dictionary that is adapted to image content. The projection of the image content to this subspace are used then to define a metric between a pixel and the neighborhood ones. Very promising experimental results show the potential of our approach.

In this paper, we are interested in using multiwavelet transforms in the context of progressive and lossless coding of multi component images. More precisely, multiwavelet decompositions that map integers to integers are considered since they guarantee a perfect reconstruction in the absence of quantizers. Generally, these decompositions are performed separately on each spectral component of a multicomponent image. Therefore, they fail to exploit the spectral redundancies. Our main contribution in this paper consists in modifying multiwavelet decompositions in order to take into account simultaneously the spatial and the spectral redundancies contained in a multicomponent image. Simulation tests carried out on natural multicomponent images show that the the generalized interband decomposition outperforms the state-of-art lossless coders.