We propose a rotationally invariant similarity measure as a modification of the well-known block matching algorithm for finding similar regions in an image or an image sequence. This algorithm can find similar patches even if they appear in several rotated or even mirrored instances. We demonstrate the application of this approach to enhance the quality of the non-local means algorithm for imag...

Introduction: Real-time cardiac MRI may be a powerful technique to assess myocardial function, especially to overcome gating difficulties in patients with arrhythmias, dyspnea or in pediatrics [1]. However, despite improvements in technology and sequences, standard real-time MRI often suffers from compromised spatiotemporal resolution. To achieve high temporal resolution (e.g. compatible with p...

In this work we derive a novel density driven diffusion scheme for image enhancement. Our approach, called D3, is a semi-local method that uses an initial structure-preserving oversegmentation step of the input image. Because of this, each segment will approximately conform to a homogeneous region in the image, allowing us to easily estimate parameters of the underlying stochastic process thus ...

This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotat...

This paper is about extending the classical Non-Local Means (NLM) denoising algorithm using general shapes instead of square patches. The use of various shapes enables to adapt to the local geometry of the image while looking for pattern redundancies. A fast FFT-based algorithm is proposed to compute the NLM with arbitrary shapes. The local combination of the different shapes relies on Stein’s ...

Super-resolution without explicit sub-pixel motion estimation is a very active subject of image reconstruction containing general motion. The Non-Local Means (NLM) method is a simple image reconstruction method without explicit motion estimation. In this paper we generalize NLM method to higher orders using kernel regression can apply to super-resolution reconstruction. The performance of the g...

This paper addresses the search for a fast and meaningful image segmentation in the context of k-means clustering. The proposed method builds on a widely-used local version of Lloyd’s algorithm, called Simple Linear Iterative Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically adjust the local search, adopting superpixel resolution dynamically to structure existent in t...

Many useful algorithms for processing images and geometry fall under the general framework of high-dimensional Gaussian filtering. This family of algorithms includes bilateral filtering and non-local means. We propose a new way to perform such filters using the permutohedral lattice, which tessellates high-dimensional space with uniform simplices. Our algorithm is the first implementation of a ...

Recent work in the domain of classification of point clouds has shown that topic models can be suitable tools for inferring class groupings in an unsupervised manner. However, point clouds are frequently subject to non-negligible amounts of sensor noise. In this paper, we analyze the effect on classification accuracy of noise added to both an artificial data set and data collected from a Light ...

Proposed the Algorithm of K-means (CPSOKM) based on Chaos Particle Swarm in order to solve the problem that K-means algorithm sensitive to initial conditions and is easy to influence the clustering effect. On the selection of the initial value problem, algorithm using particle swarm algorithm to balance the random value uncertainty, and then by introducing a chaotic sequence, the particles move...