From multi-scale decomposition to non-multi-scale decomposition methods: a comprehensive survey of image fusion techniques and its applications
Image fusion is a well-recognized and a conventional field of image processing. Image
fusion provides an efficient way of enhancing and combining pixel-level data resulting in …
fusion provides an efficient way of enhancing and combining pixel-level data resulting in …
Image denoising review: From classical to state-of-the-art approaches
At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …
Fast finite shearlet transform
In recent years it has turned out that shearlets have the potential to retrieve directional
information so that they became interesting for many applications. Moreover the continuous …
information so that they became interesting for many applications. Moreover the continuous …
Learning the invisible: A hybrid deep learning-shearlet framework for limited angle computed tomography
The high complexity of various inverse problems poses a significant challenge to model-
based reconstruction schemes, which in such situations often reach their limits. At the same …
based reconstruction schemes, which in such situations often reach their limits. At the same …
Digital image noise estimation using DWT coefficients
Noise type and strength estimation are important in many image processing applications like
denoising, compression, video tracking, etc. There are many existing methods for estimation …
denoising, compression, video tracking, etc. There are many existing methods for estimation …
Artifact-free wavelet denoising: Non-convex sparse regularization, convex optimization
Y Ding, IW Selesnick - IEEE signal processing letters, 2015 - ieeexplore.ieee.org
Algorithms for signal denoising that combine wavelet-domain sparsity and total variation
(TV) regularization are relatively free of artifacts, such as pseudo-Gibbs oscillations …
(TV) regularization are relatively free of artifacts, such as pseudo-Gibbs oscillations …
Torchradon: Fast differentiable routines for computed tomography
M Ronchetti - arXiv preprint arXiv:2009.14788, 2020 - arxiv.org
This work presents TorchRadon--an open source CUDA library which contains a set of
differentiable routines for solving computed tomography (CT) reconstruction problems. The …
differentiable routines for solving computed tomography (CT) reconstruction problems. The …
Total variation denoising via the Moreau envelope
I Selesnick - IEEE Signal Processing Letters, 2017 - ieeexplore.ieee.org
Total variation denoising is a nonlinear filtering method well suited for the estimation of
piecewise-constant signals observed in additive white Gaussian noise. The method is …
piecewise-constant signals observed in additive white Gaussian noise. The method is …
Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image
S Zhou, J Shi, J Zhu, Y Cai, R Wang - Biomedical Signal Processing and …, 2013 - Elsevier
To augment the classification accuracy of the ultrasound computer-aided diagnosis (CAD)
for breast tumor detection based on texture feature, we proposed to extract texture feature …
for breast tumor detection based on texture feature, we proposed to extract texture feature …
Introduction to shearlets
G Kutyniok, D Labate - Shearlets: Multiscale analysis for multivariate data, 2012 - Springer
Shearlets emerged in recent years among the most successful frameworks for the efficient
representation of multidimensional data. Indeed, after it was recognized that traditional …
representation of multidimensional data. Indeed, after it was recognized that traditional …