Zobrazeno 1 - 10
of 1 448
pro vyhledávání: '"Unser, Michael"'
We propose a regularization scheme for image reconstruction that leverages the power of deep learning while hinging on classic sparsity-promoting models. Many deep-learning-based models are hard to interpret and cumbersome to analyze theoretically. I
Externí odkaz:
http://arxiv.org/abs/2407.06608
Optoacoustic (OA) imaging combined with reversibly photoswitchable proteins has emerged as a promising technology for the high-sensitivity and multiplexed imaging of cells in live tissues in preclinical research. Through carefully-designed illuminati
Externí odkaz:
http://arxiv.org/abs/2405.15360
We consider a large class of shallow neural networks with randomly initialized parameters and rectified linear unit activation functions. We prove that these random neural networks are well-defined non-Gaussian processes. As a by-product, we demonstr
Externí odkaz:
http://arxiv.org/abs/2405.10229
Autor:
Parhi, Rahul, Unser, Michael
We investigate the function-space optimality (specifically, the Banach-space optimality) of a large class of shallow neural architectures with multivariate nonlinearities/activation functions. To that end, we construct a new family of Banach spaces d
Externí odkaz:
http://arxiv.org/abs/2310.03696
Autor:
Parhi, Rahul, Unser, Michael
Publikováno v:
SIAM Journal on Mathematical Analysis, vol. 56, no. 4, pp. 4662-4686, 2024
We investigate the distributional extension of the $k$-plane transform in $\mathbb{R}^d$ and of related operators. We parameterize the $k$-plane domain as the Cartesian product of the Stiefel manifold of orthonormal $k$-frames in $\mathbb{R}^d$ with
Externí odkaz:
http://arxiv.org/abs/2310.01233
We propose to learn non-convex regularizers with a prescribed upper bound on their weak-convexity modulus. Such regularizers give rise to variational denoisers that minimize a convex energy. They rely on few parameters (less than 15,000) and offer a
Externí odkaz:
http://arxiv.org/abs/2308.10542
Optical projection tomography (OPT) is a powerful tool for biomedical studies. It achieves 3D visualization of mesoscopic biological samples with high spatial resolution using conventional tomographic-reconstruction algorithms. However, various artif
Externí odkaz:
http://arxiv.org/abs/2309.16677
We show that structural information can be extracted from single molecule localization microscopy (SMLM) data. More precisely, we reinterpret SMLM data as the measures of a phaseless optical diffraction tomography system for which the illumination so
Externí odkaz:
http://arxiv.org/abs/2307.09261
Over the years, computational imaging with accurate nonlinear physical models has drawn considerable interest due to its ability to achieve high-quality reconstructions. However, such nonlinear models are computationally demanding. A popular choice f
Externí odkaz:
http://arxiv.org/abs/2307.02043
In supervised learning, the regularization path is sometimes used as a convenient theoretical proxy for the optimization path of gradient descent initialized from zero. In this paper, we study a modification of the regularization path for infinite-wi
Externí odkaz:
http://arxiv.org/abs/2303.17805