Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, and many others. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part respectively. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial-spectral total variation (SSTV) regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell_1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regulariztion has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier (ALM) method. Finally, extensive experiments on simulated and real-world noise HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure

Spontaneous Subtle Expression Detection and Recognition based on Facial Strain

Optical strain is an extension of optical flow that is capable of quantifying subtle changes on faces and representing the minute facial motion intensities at the pixel level. This is computationally essential for the relatively new field of spontaneous micro-expression, where subtle expressions can be technically challenging to pinpoint. In this paper, we present a novel method for detecting and recognizing micro-expressions by utilizing facial optical strain magnitudes to construct optical strain features and optical strain weighted features. The two sets of features are then concatenated to form the resultant feature histogram. Experiments were performed on the CASME II and SMIC databases. We demonstrate on both databases, the usefulness of optical strain information and more importantly, that our best approaches are able to outperform the original baseline results for both detection and recognition tasks. A comparison of the proposed method with other existing spatio-temporal feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to Signal Processing: Image Communication journa

A wide-field spectroscopic survey of the cluster of galaxies Cl0024+1654: I. The catalogue

We present the catalogue of a wide-field CFHT/WHT spectroscopic survey of the lensing cluster Cl0024+1654 at z=0.395. This catalogue contains 618 new spectra, of which 581 have identified redshifts. Adding redshifts available from the literature, the final catalogue contains data for 687 objects with redshifts identified for 650 of them. 295 galaxies have redshifts in the range 0.37<z<0.41, i. e. are cluster members or lie in the immediate neighbourhood of the cluster. The area covered by the survey is 21x25 arcmin2 in size, corresponding to 4x4.8 h^-2 Mpc2 at the cluster redshift. The survey is 45% complete down to V=22 over the whole field covered; within 3 arcmin of the cluster centre the completeness exceeds 80% at the same magnitude. A detailed completeness analysis is presented. The catalogue gives astrometric position, redshift, V magnitude and V-I colour, as well as the equivalent widths for a number of lines. Apart from the cluster Cl0024+1654 itself, three other structures are identified in redshift space: a group of galaxies at z=0.38, just in front of Cl0024+1654 and probably interacting with it, a close pair of groups of galaxies at z~0.495 and an overdensity of galaxies at z~0.18 with no obvious centre. The spectroscopic catalogue will be used to trace the three-dimensional structure of the cluster Cl0024+1654 as well as study the physical properties of the galaxies in the cluster and in its environment.Comment: 14 pages - figures included - A&A (re)submitted versio

Caractérisation des nano-particules dans les arcs d'ouverture.

National audienc

Continuous Hierarchical Representations with Poincar\'e Variational Auto-Encoders

The variational auto-encoder (VAE) is a popular method for learning a generative model and embeddings of the data. Many real datasets are hierarchically structured. However, traditional VAEs map data in a Euclidean latent space which cannot efficiently embed tree-like structures. Hyperbolic spaces with negative curvature can. We therefore endow VAEs with a Poincar\'e ball model of hyperbolic geometry as a latent space and rigorously derive the necessary methods to work with two main Gaussian generalisations on that space. We empirically show better generalisation to unseen data than the Euclidean counterpart, and can qualitatively and quantitatively better recover hierarchical structures.Comment: Advances in Neural Information Processing System

Regenerating Arbitrary Video Sequences with Distillation Path-Finding

If the video has long been mentioned as a widespread visualization form, the animation sequence in the video is mentioned as storytelling for people. Producing an animation requires intensive human labor from skilled professional artists to obtain plausible animation in both content and motion direction, incredibly for animations with complex content, multiple moving objects, and dense movement. This paper presents an interactive framework to generate new sequences according to the users' preference on the starting frame. The critical contrast of our approach versus prior work and existing commercial applications is that novel sequences with arbitrary starting frame are produced by our system with a consistent degree in both content and motion direction. To achieve this effectively, we first learn the feature correlation on the frameset of the given video through a proposed network called RSFNet. Then, we develop a novel path-finding algorithm, SDPF, which formulates the knowledge of motion directions of the source video to estimate the smooth and plausible sequences. The extensive experiments show that our framework can produce new animations on the cartoon and natural scenes and advance prior works and commercial applications to enable users to obtain more predictable results.Comment: This paper has been accepted for publication on IEEE Transactions on Visualization and Computer Graphics (TVCG), January 2023. Project website: http://graphics.csie.ncku.edu.tw/SDP

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow

Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables. Amortized gradient-based learning of SCFMs is challenging as most approaches targeting discrete variables rely on their continuous relaxations---which can be intractable in SCFMs, as branching on relaxations requires evaluating all (exponentially many) branching paths. Tractable alternatives mainly combine REINFORCE with complex control-variate schemes to improve the variance of naive estimators. Here, we revisit the reweighted wake-sleep (RWS) (Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it outperforms current state-of-the-art methods in learning SCFMs. Further, in contrast to the importance weighted autoencoder, we observe that RWS learns better models and inference networks with increasing numbers of particles. Our results suggest that RWS is a competitive, often preferable, alternative for learning SCFMs.Comment: Tuan Anh Le and Adam R. Kosiorek contributed equally; accepted to Uncertainty in Artificial Intelligence 201

Chronic Hepatitis B Prevalence Among Foreign‐Born and U.S.‐Born Adults in the United States, 1999‐2016

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154388/1/hep30831-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154388/2/hep30831.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154388/3/hep30831_am.pd