F
Florian Luisier
Researcher at Harvard University
Publications - 34
Citations - 2692
Florian Luisier is an academic researcher from Harvard University. The author has contributed to research in topics: Wavelet transform & Wavelet. The author has an hindex of 16, co-authored 32 publications receiving 2462 citations. Previous affiliations of Florian Luisier include Hoffmann-La Roche & École Polytechnique Fédérale de Lausanne.
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Journal ArticleDOI
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding
TL;DR: An interscale orthonormal wavelet thresholding algorithm is described based on this new approach and its near-optimal performance is described by comparing it with the results of three state-of-the-art nonredundant denoising algorithms on a large set of test images.
Journal ArticleDOI
Image Denoising in Mixed Poisson–Gaussian Noise
TL;DR: The denoising process is expressed as a linear expansion of thresholds (LET) that is optimized by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE) derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate).
Journal ArticleDOI
The SURE-LET Approach to Image Denoising
Thierry Blu,Florian Luisier +1 more
TL;DR: It is shown that a denoising algorithm merely amounts to solving a linear system of equations which is obviously fast and efficient, and the very competitive results obtained by performing a simple threshold on the undecimated Haar wavelet coefficients show that the SURE-LET principle has a huge potential.
Journal ArticleDOI
Fast interscale wavelet denoising of Poisson-corrupted images
TL;DR: The authors' non-redundant interscale wavelet thresholding outperforms standard variance-stabilizing schemes, even when the latter are applied in a translation-invariant setting (cycle-spinning).
Proceedings ArticleDOI
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
TL;DR: In this article, different ways of performing spatial and temporal pooling, feature normalization, choice of CNN layers as well as choice of classifiers are explored for event detection in videos using convolutional neural networks (CNNs) trained for image classification.