Search Results for author:
Found 16 papers, 6 papers with code
Whiteness-based bilevel learning of regularization parameters in imaging
We consider an unsupervised bilevel optimization strategy for learning regularization parameters in the context of imaging inverse problems in the presence of additive white Gaussian noise.
Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc
The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines.
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers
The spatial resolution of images of living samples obtained by fluorescence microscopes is physically limited due to the diffraction of visible light, which makes the study of entities of size less than the diffraction barrier (around 200 nm in the x-y plane) very challenging.
Beyond $\ell_1$ sparse coding in V1
Compared to other methods, soft thresholding achieves this level of sparsity at the expense of much degraded reconstruction performance, that more likely than not is not acceptable in biological vision.
Scaled, inexact and adaptive generalized FISTA for strongly convex optimization
The proposed algorithm is combined with an adaptive (non-monotone) backtracking strategy, which allows for the adjustment of the algorithmic step-size along the iterations in order to improve the convergence speed.
Deblurring
Image Denoising
Optimization and Control
A cortical-inspired sub-Riemannian model for Poggendorff-type visual illusions
We consider Wilson-Cowan-type models for the mathematical description of orientation-dependent Poggendorff-like illusions.
Non-convex Super-resolution of OCT images via sparse representation
We propose a non-convex variational model for the super-resolution of Optical Coherence Tomography (OCT) images of the murine eye, by enforcing sparsity with respect to suitable dictionaries learnt from high-resolution OCT data.
On the inverse Potts functional for single-image super-resolution problems
We consider a variational model for single-image super-resolution based on the assumption that the image gradient of the target image is sparse.
Image and Video Processing Numerical Analysis Numerical Analysis 65F22, 65K10 G.1.6; G.1.10; I.4.3; I.4.4; I.4.5; I.4.6
Cortical-inspired Wilson-Cowan-type equations for orientation-dependent contrast perception modelling
We consider the evolution model proposed in [9, 6] to describe illusory contrast perception phenomena induced by surrounding orientations.
Variational Osmosis for Non-linear Image Fusion
We propose a new variational model for non-linear image fusion.
Space-adaptive anisotropic bivariate Laplacian regularization for image restoration
In this paper we present a new regularization term for variational image restoration which can be regarded as a space-variant anisotropic extension of the classical isotropic Total Variation (TV) regularizer.
A cortical-inspired model for orientation-dependent contrast perception: a link with Wilson-Cowan equations
We consider a differential model describing neuro-physiological contrast perception phenomena induced by surrounding orientations.
Anisotropic osmosis filtering for shadow removal in images
We present an anisotropic extension of the isotropic osmosis model that has been introduced by Weickert et al.~(Weickert, 2013) for visual computing applications, and we adapt it specifically to shadow removal applications.
Analysis of PDEs 68U10, 94A08, 49K20, 65M06,
Unveiling the invisible - mathematical methods for restoring and interpreting illuminated manuscripts
The last fifty years have seen an impressive development of mathematical methods for the analysis and processing of digital images, mostly in the context of photography, biomedical imaging and various forms of engineering.
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images
We consider the problem of scale detection in images where a region of interest is present together with a measurement tool (e. g. a ruler).
Bilevel approaches for learning of variational imaging models
We review some recent learning approaches in variational imaging, based on bilevel optimisation, and emphasize the importance of their treatment in function space.
