Search

International audience; Active contours (also known as snakes) have shown their ability to introduce regularity on image segmentation. In contrast with level-set approaches, the active contours techniques based on a contour parameterization are able to maintain the initial topology of the area of interest. For this reason, it has been used in recent medical research for diaphragm segmentation. Most of the on-line codes for 2D/3D segmentation, as well as built-in Matlab toolboxes are based on level-set methods. Moreover, in the literature, the implementation details of active contours methods with meshes in three dimensions are tight, making tedious any reproduction of these techniques. In this paper, we give some details of the implementation of active contours in 2D/3D with meshes, especially about the choice of the use of a 2D/3D mesh and its refinement. We also explore the choice of the parameters with a quantitative study of their influence on the segmentation results. The 3D segmentation method has been applied to CT scan images of the lungs.

International audience; We propose to detect defects in old movies, as the first step of a larger framework of old movies restoration by inpainting techniques. The specificity of our work is to learn a film restorer's expertise from a pair of sequences, composed of a movie with defects, and the same movie which was semi-automatically restored with the help of a specialized software. In order to detect those defects with minimal human interaction and further reduce the time spent for a restoration, we feed a U-Net with consecutive defective frames as input to detect the unexpected variations of pixel intensity over space and time. Since the output of the network is a mask of defect location, we first have to create the dataset of mask frames on the basis of restored frames from the software used by the film restorer, instead of classical synthetic ground truth, which is not available. These masks are estimated by computing the absolute difference between restored frames and defectuous frames, combined with thresholding and morphological closing. Our network succeeds in automatically detecting real defects with more precision than the manual selection with an all-encompassing shape, including some the expert restorer could have missed for lack of time.

International audience; This paper focuses on the denoising of chrominance channels of color images. We propose a variational framework involving TV reg-ularization that modifies the chrominance channel while preserving the input luminance of the image. The main issue of such a problem is to ensure that the denoised chrominance together with the original luminance belong to the RGB space after color format conversion. Standard methods of the literature simply truncate the converted RGB values, which lead to a change of hue in the denoised image. In order to tackle this issue, a " RGB compatible " chrominance range is defined on each pixel with respect to the input luminance. An algorithm to compute the orthogonal projection onto such a set is then introduced. Next, we propose to extend the CLEAR debiasing technique to avoid the loss of colourfulness produced by TV regularization. The benefits of our approach with respect to state-of-the-art methods are illustrated on several experiments.

Colorization of gray-scale images relies on prior color information. Exemplar-based methods use a color image as source of such information. Then the colors of the source image are transferred to the gray-scale target image. In the literature, this transfer is mainly guided by texture descriptors. Face images usually contain few texture so that the common approaches frequently fail. In this paper, we propose a new method taking the geometric structure of the images rather their texture into account such that it is more reliable for faces. Our approach is based on image morphing and relies on the YUV color space. First, a correspondence mapping between the luminance Y channel of the color source image and the gray-scale target image is computed. This mapping is based on the time discrete metamorphosis model suggested by Berkels, Effland and Rumpf. We provide a new finite difference approach for the numerical computation of the mapping. Then, the chrominance U,V channels of the source image are transferred via this correspondence map to the target image. A possible postprocessing step by a variational model is developed to further improve the results. To keep the contrast special attention is paid to make the postprocessing unbiased. Our numerical experiments show that our morphing based approach clearly outperforms state-of-the-art methods.

International audience; This paper deals with the difficult problem of video colorization. Methods in the literature are generally based on spatio-temporal video blocks, or on frame to frame color propagation methods, each technique having its own advantages and drawbacks. In this paper , we present both a novel automatic frame-to-frame propagation approach and an interactive correction method within a variational framework. The proposed method propagates colors from an initial colorized frame to the whole grayscale video sequence. The automatic propagation results may be visually unsuitable in some cases. To overcome this limitation, a spatio-temporal functional with a user-guided correction is introduced. Two primal-dual algorithms are designed to solve the proposed variational models. Numerical results show the efficiency and the potentiality of the proposed approach in comparison with state-of-the-art methods.

International audience; The aim of this paper is twofold. First, we propose a new method for enhancing the contrast of gray-value images. We use the difference of the average local contrast measures between the original and the enhanced images within a variational framework. This enables the user to control the contrast level and the scale of the enhanced details intuitively. Moreover, our model avoids large modifications of the original image histogram. Thereby it preserves the global illumination of the scene and can cope with large areas having similar gray values. The minimizer of the proposed functional is computed by a gradient descent algorithm in connection with a polynomial approximation of the average local contrast measure. The polynomial approximation is done via Bernstein polynomials and leads to to a speed up of the algorithm by applying fast Fourier transforms. In the second part, the approach is extended to a variational enhancement method for color images. The model approximately preserves the hue of the original image and includes additionally a total variation term to correct possible noise. The method requires no post-or pre-processing. The minimization problem is solved with a hybrid primal-dual algorithm. Numerical experiments demonstrate the efficiency and the flexibility of the proposed approaches in comparison with state-of-the-art methods.

International audience; This paper proposes a novel model for contrast enhancement of RGB images. The average local contrast measure is increased within a variational framework which preserves the hue of the original image by coupling the channels. The user is enabled to intuitively control the level of the contrast as well as the scale of the enhanced details. Moreover, our model avoids large modifications of the original image his-togram and thereby preserves the global illumination of the scene. The minimizer of the proposed functional is computed by a hybrid primal-dual algorithm. Numerical experiments show the reliability of the proposed approach in comparison with state-of-the-art methods.

International audience; This paper is concerned with a problem arising when editing color images, namely the Luminance-Hue Specification. This problem often occurs when converting an edited image in a given color-space to RGB. Indeed, the colors often get out of the standard range of the RGB space which is commonly used by most of display hardwares. Simple truncations lead to inconsistency in the hue and luminance of the edited image. We formalize and describe this problem from a geometrical point of view. A fast algorithm to solve the considered problem is given. We next focus on its application to image colorization in the RGB color-space while most of the methods use other ones. Using directly the three RGB channels, our model avoids artifact effects which appear with other color-spaces. Finally a variational model that regularizes color images while dealing with Luminance-Hue Specification problem is proposed.

This paper addresses the topic of image colorization that consists in converting a gray-scale image into a color one. In the literature, there exist two main types of approaches to tackle this problem. The first one is the manual methods where the color information is given by some scribbles drawn by the user on the image. The interest of these approaches comes from the interactions with the user that can put any color he wants. Nevertheless, when the scene is complex many scribbles must be drawn and the interactive process becomes tedious and time-consuming. The second category of approaches is the exemplar-based methods that require a color image as input. Once the example image is given, the colorization is generally fully automatic. A limitation of these methods is that the example image needs to contain all the desired colors in the final result. In this paper, we propose a new framework that unifies these two categories of approaches into a joint variational model. Our approach is able to take into account information coming from any colorization method among these two categories. Experiments and comparisons demonstrate that the proposed approach provides competitive colorization results compared to state-of-the-art methods.

International audience; This paper deals with the problem of image colorization. A model including total variation regularization is proposed. Our approach colorizes directly the three RGB channels, while most existing methods were only focusing on the two chrominance channels. By using the three channels, our approach is able to better preserve color consistency. Our model is non convex, but we propose an efficient primal-dual like algorithm to compute a local minimizer. Numerical examples illustrate the good behavior of our algorithm with respect to state-of-the-art methods.

International audience; This paper provides a new method to colorize gray-scale images. While the computation of the luminance channel is directly performed by a linear transformation, the colorization process is an ill-posed problem that requires some priors. In the literature two classes of approach exist. The first class includes manual methods that need the user to manually add colors on the image to colorize. The second class includes exemplar-based approaches where a color image, with a similar semantic content, is provided as input to the method. These two types of priors have their own advantages and drawbacks. In this paper, a new variational framework for exemplar-based colorization is proposed. A nonlocal approach is used to find relevant color in the source image in order to suggest colors on the gray-scale image. The spatial coherency of the result as well as the final color selection is provided by a nonconvex variational framework based on a total variation. An efficient primal-dual algorithm is provided, and a proof of its convergence is proposed. In this work, we also extend the proposed exemplar-based approach to combine both exemplar-based and manual methods. It provides a single framework that unifies advantages of both approaches. Finally, experiments and comparisons with state-of-the-art methods illustrate the efficiency of our proposal. 1. Introduction. The colorization of a gray-scale image consists of adding color information to it. It is useful in the entertainment industry to make old productions more attractive. The reverse operation is based on perceptual assumptions and is today an active research area [28], [13], [37]. Colorization can also be used to add information in order to help further analysis of the image by a user (e.g., sensor fusion [43]). It can also be used for art restoration ; see, e.g., [17] or [41]. It is an old subject that began with the ability of screens and devices to display colors. A seminal approach consists in mapping each level of gray into a color-space [18]. Nevertheless, all colors cannot be recovered without an additional prior. In the existing approaches, priors can be added in two ways: with a direct addition of color on

International audience; Color image enhancement is a challenging task in digital imaging with many applications. This paper contributes to image enhancement methods. We propose a new variational model for color improvement in the RGB space based on a desired target intensity image. Our model improves the visual quality of the color image while it preserves the range and takes the hue of the original, badly exposed image into account without amplifying its color artifacts. To approximate the hue of the original image we use the fact that affine transforms are hue preserving. To cope with the noise in the color channels we design a particular coupled TV regularization term. Since the target intensity of the image is unaltered our model respects important image structures. Numerical results demonstrate the very good performance of our method.