Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models

Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches

Teledetection des contours lineaires

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

An adaptive total variation method for speckle reduction in medical ultrasound imaging

International audienceTo reduce the speckle noise and preserve the edge information, a novel algorithm based on an adaptive total variation method is proposed. The smoothing process uses adaptive windows whose shapes, sizes and orientations vary with image structures. Adaptive window calculate instantaneous Lagrange coefficient for edge areas with more accuracy avoiding smoothing these regions and performing well in large ones. Experimental results show that, the performance of the proposed method is satisfactory in terms of both speckle suppression and preservation of medical ultrasound image details

A new fractional-order variational model for speckled de-noising

International audienceIn this paper, a novel speckled image de-noising algorithm is proposed. A fractional-order multiplicative variational model is included as a multiplicative constraint in the regularization problem thereby the appropriate regularization parameter will be controlled by the optimization process itself. An adaptive selection method based on image regions property is used for the selection of the appropriate fractional-order value. The proposed algorithm not only overcomes the disadvantage of generating artificial edges but also has the advantage of de-noising and edges preservation.Experimental results show that the fractional order multiplicative variational model can improve the Peak Signal to Noise Ratio (PSNR) of image, preserve image structures and overcomes the disadvantage of generating artificial edges in the de-noising process

A new speckle filtering method for ultrasound images based on a weighted multiplicative total variation

International audienceUltrasound images are corrupted by a multiplicative noise - the speckle - which makes hard high level image analysis. In order to solve the difficulty of designing a filter for an effective speckle removing, we propose a new approach for de-noising images while preserving important features. This method combines a data misfit function based on Loupas et al. model and a Weighted Total Variation (WTV) function as a multiplicative factor in the cost functional. The de-noising process is performed using a multiplicative regularization method through an adaptive window whose shapes, sizes and orientations vary with the image structure. Instead of performing the smoothing uniformly, the process is achieved in preferred orientations, more in homogeneous areas than in detailed ones to preserve region boundaries while reducing speckle noise within regions. Quantitative results on synthetic and real images have demonstrated the efficiency and the robustness of the proposed method compared to well-established and state-of-the-art methods. The speckle is removed while edges and structural details of the image are preserved

Polygonum cuspidatum Siebold & Zucc.

https://thekeep.eiu.edu/herbarium_specimens_byname/10526/thumbnail.jp

Active learning for improving a soft subspace clustering algorithm

International audienceIn this paper a new soft subspace clustering algorithm is proposed. It is an iterative algorithm based on the minimization of a new objective function. The classification approach is developed by acting at three essential points. The first one is related to an initialization step; we suggest to use a multi-class support vector machine (SVM) for improving the initial classification parameters. The second point is based on the new objective function. It is formed by a separation term and compactness ones. The density of clusters is introduced in the last term to yield different cluster shapes. The third and the most important point consists in an active learning with SVM incorporated in the classification process. It allows a good estimation of the centers and the membership degrees and a speed convergence of the proposed algorithm. The developed approach has been tested to classify different synthetic datasets and real images databases. Several indices of performance have been used to demonstrate the superiority of the proposed method. Experimental results have corroborated the effectiveness of the proposed method in terms of good quality and optimized runtime

Quality Evaluation Algorithm: New Structural Similarity by Using Distance Transform Approach and Gradient Similarity

International audienc

3D saliency guided deep quality predictor for no-reference stereoscopic images

International audienc