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...

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 ...

We propose a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of contextual self-dissimilarity reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Mean...

We propose a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of contextual self-dissimilarity reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Mean...

Image fusion and denoising have been widely researched as separate techniques for the past few decades. Most of the fusion techniques fuse the images with the assumption that images are nonnoisy. But in many practical applications, especially, in the case of satellite images this assumption fails. In this paper, a novel technique based on nonlocal means filter in conjunction with multiresolutio...

We show that the popular Non-Local Means method for image denoising can be implemented exactly, easily and with lower computation time using convolutions. Also, our algorithm allows the use of infinite-size non-binary patches, which improves the denoising quality.