Denoising is an essential application to improve image quality, especially in medical imaging. This paper introduces an example and patch-based learning method for reducing Gaussian noise and Poisson noise which often appear in medical imaging modalities using ionizing radiation. In the proposed method, denoising is performed by learning the regression model based on a set of the nearest neighbors of a given noisy patch, with the help of a given set of standard images. The method is evaluated and compared to several state-of-the-art denoising methods. The obtained results confirm its efficiency, especially for heavy noise.

In this paper, we propose a novel example-based method for denoising and super-resolution of medical images. The objective is to estimate a high-resolution image from a single noisy low-resolution image, with the help of a given database of high and low-resolution image patch pairs. Denoising and super-resolution in this paper is performed on each image patch. For each given input low-resolution patch, its high-resolution version is estimated based on finding a nonnegative sparse linear representation of the input patch over the low-resolution patches from the database, where the coefficients of the representation strongly depend on the similarity between the input patch and the sample patches in the database. The problem of finding the nonnegative sparse linear representation is modeled as a nonnegative quadratic programming problem. The proposed method is especially useful for the case of noise-corrupted and low-resolution image. Experimental results show that the proposed method outperforms other state-of-the-art super-resolution methods while effectively removing noise.

Medical images are often corrupted by random noise, leading to undesirable visual quality. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. In this paper, we propose a novel learning method for the reduction of Gaussian noise of Computed Tomography (CT) image and Rician noise of Magnetic Resonance Imaging (MRI) image based on a given set of standard images and the Kernel Ridge Regression (KRR). Experimental results demonstrate the outperformance of the proposed technique over various other methods in terms of both objective and subjective evaluations.

This paper presents a fast method for detecting textured objects moving in a video sequence. It is based on a known background estimation and a fixed camera position. The algorithm is able to detect the presence of moving objects and locates them on-line. It is a frame-by-frame method. First, a difference image is computed from the background and the current frame. This yields three classes of pixels, those for which something changed with respect to the background, those for which nothing changed, and finally the pixels for which no decision can be made. Then an a contrario model allows an automatic clustering, by using adapted rectangular windows, of the pixels for which changes have been detected. If necessary, these regions are corrected in order to better fit the moving objects' boundaries. Experimental results show that the algorithm is very robust with respect to noise and to the quality of the background estimation. The choice of the model parameters is quite natural and user friendly. The algorithm has been successfully tested on video sequences coming from different databases, including indoor and outdoor sequences.

In this article we present a real time algorithm for detecting moving objects in a video sequence taken with a fixed camera. When a background estimation is known, the algorithm is able to detect moving objects and locates them approximately. The method is based on a comparison between each image and the background. We use an a contrario model, assuming that moving objects yield locally major changes in the topographic map. Experimental results show that the algorithm is very robust to noise and to the background estimation. Moreover, the algorithm needs few parameters, which are easy to estimate.

In this article we present a real time algorithm for detecting moving objects in a video sequence taken with a fixed camera. When a background estimation is known, the algorithm is able to detect moving objects and locates them approximately. The method is based on a comparison between each single image of the sequence and the background, this gives us a fast algorithm which can be applied on a video sequence with a rather high frame rate, but on the other hand, we are also able to work with sequences having a very low frame rate. We use an a contrario model, assuming that moving objects yield locally major changes in the topographic map. Experimental results show that the algorithm is very robust to noise and to the background estimation. Moreover, the algorithm needs few parameters, which are easy to estimate.

In this paper, we investigate some recent active contour models used in image and video segmentation and we transpose them into a discrete form to apply the iterated conditional modes (ICM) algorithm. This work can be seen as an extension of the recent work of T. Chan and B. Song for other functional than the Mumford-Shah/Chan-Vese one. We investigate it for video segmentation and tracking applications.

Recently, the use of the heatlike equation was extended to the projective case in order to find a projective analysis of curves and images; unfortunately, this formulation leads to a fifth-order partial differential equation (PDE) that is not easy to implement. Thanks to the use of a three-dimensional (3-D)homogeneous representation of a picture, we present here an alternative. Roughly speaking, it is a kind of decomposition of the heatlike formulation with well-posed second-order PDE's. The number of parameters goes from one to three (the scale parameter and two direction parameters). Moreover, this study allows us to propose a simplified multiscale analysis, which is given by an unique PDE (one parameter), for the subgroup of the projective transformations associated, up to a nonzero scalar factor, to an orthogonal 3 x 3 matrix.

Thanks to the use of a 3D homogeneous representation of a picture, we present a multiscale analysis (T/sub t//sup P/), t/spl isin/R/sup +/, P/spl isin//spl sime/S/sup 2/, which is invariant under the projective group: let g be a picture in the plane; for every projective transformation A, there exist t'=t'(A, t), Q=Q(A, P) such that A(T/sub t'//sup Q/g)=T/sub t//sup P/(Ag). Moreover, this study allows us to propose simplified multiscale analysis, which are given by a unique PDE, for subgroups of the projective group: the subgroups of the projective transformations which leave invariant a line in the plane; the subgroup of the projective transformations associated, up to a non-zero scalar factor, to an orthogonal 3,3 matrix.

We introduce a discrete scheme using a morphological image processing approach. After briefly presenting the axiomatic approach of image processing and the mean curvature motion partial differential equation (PDE), the properties of the proposed scheme are studied. We show that this morphological scheme performs mean curvature evolution on a gray level image. Then we notice that the same scheme can be applied to forms. To conclude we present experimental results.>

We introduce in this paper a discrete scheme using a morphological image processing approach. After briefly presenting the axiomatic approach of image processing and the mean curvature motion partial differential equation, the properties of the proposed scheme are studied. We show that this morphological scheme performs mean curvature evolution on a gray level image.