Brief review of image denoising techniques
L Fan, F Zhang, H Fan, C Zhang - Visual Computing for Industry …, 2019 - Springer
With the explosion in the number of digital images taken every day, the demand for more
accurate and visually pleasing images is increasing. However, the images captured by …
accurate and visually pleasing images is increasing. However, the images captured by …
A review on CT image noise and its denoising
CT imaging is widely used in medical science over the last decades. The process of CT
image reconstruction depends on many physical measurements such as radiation dose …
image reconstruction depends on many physical measurements such as radiation dose …
DC programming and DCA: thirty years of developments
HA Le Thi, T Pham Dinh - Mathematical Programming, 2018 - Springer
The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Recent research has demonstrated that feature attribution methods for deep networks can
themselves be incorporated into training; these attribution priors optimize for a model whose …
themselves be incorporated into training; these attribution priors optimize for a model whose …
Disciplined convex-concave programming
In this paper we introduce disciplined convex-concave programming (DCCP), which
combines the ideas of disciplined convex programming (DCP) with convex-concave …
combines the ideas of disciplined convex programming (DCP) with convex-concave …
Hierarchical perception adversarial learning framework for compressed sensing MRI
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI)
because it leads to patient discomfort and motion artifacts. Although several MRI techniques …
because it leads to patient discomfort and motion artifacts. Although several MRI techniques …
A scale-invariant approach for sparse signal recovery
In this paper, we study the ratio of the L_1 and L_2 norms, denoted as L_1/L_2, to promote
sparsity. Due to the nonconvexity and nonlinearity, there has been little attention to this scale …
sparsity. Due to the nonconvexity and nonlinearity, there has been little attention to this scale …
Learning explainable models using attribution priors
Two important topics in deep learning both involve incorporating humans into the modeling
process: Model priors transfer information from humans to a model by regularizing the …
process: Model priors transfer information from humans to a model by regularizing the …
Hyperspectral Image Restoration via Global L1-2 Spatial–Spectral Total Variation Regularized Local Low-Rank Tensor Recovery
Hyperspectral images (HSIs) are usually corrupted by various noises, eg, Gaussian noise,
impulse noise, stripes, dead lines, and many others. In this article, motivated by the good …
impulse noise, stripes, dead lines, and many others. In this article, motivated by the good …
Truncated Models for Sparse Recovery and Rank Minimization
TH Ma, Y Lou, TZ Huang - SIAM Journal on Imaging Sciences, 2017 - SIAM
We study a truncated difference of l_1 and l_2 norms as a nonconvex metric for recovering
sparse vectors and low-rank matrices from linear measurements. By discarding large …
sparse vectors and low-rank matrices from linear measurements. By discarding large …