29 research outputs found
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Social media reveals consistently disproportionate tourism pressure on a threatened marine vertebrate
Establishing how wildlife viewing pressure is distributed across individual animals within a population can inform the management of this activity, and ensure targeted individuals or groups are sufficiently protected. Here, we used social media data to quantify whether tourism pressure varies in a loggerhead sea turtle Caretta caretta population and elucidate the potential implications. Laganas Bay (Zakynthos, Greece) supports both breeding (migratory, and hence transient) and foraging (resident) turtles, with turtle viewing representing a major component of the tourism industry. Social media entries spanning two seasons (April to November, 2018 and 2019) were evaluated, and turtles were identified via photo-identification. For the 2 years, 1684 and 2105 entries of 139 and 122 unique turtles were obtained from viewings, respectively (boats and underwater combined). However, while residents represented less than one-third of uniquely identified turtles, they represented 81.9 and 87.9% of all entries. Even when the seasonal breeding population was present (May to July), residents represented more than 60% of entries. Notably, the same small number of residents (<10), mostly males, were consistently viewed in both years; however, different individuals were targeted by boats versus underwater. Thus, turtles appear to remain in the area despite high viewing intensity, possibly indicating low disturbance. However, photo-identification records revealed a high risk of propeller and boat strike to residents (30%) leading to trauma and mortality. To reduce this threat, we recommend the compulsory use of propeller guards for all boats, compliance with speed regulations and the creation of temporary ârefugeâ zones for resident animals at viewing hotspots, with these suggestions likely being relevant for other wildlife with similar population dynamics. In conclusion, social media represents a useful tool for monitoring individuals at a population scale, evaluating the pressure under which they are placed, and providing sufficient data to refine wildlife viewing guidelines and/or zoning. © 2020 The Authors. Animal Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of Londo
Bilevel Training Schemes in Imaging for Total Variation--Type Functionals with Convex Integrands
In the context of image processing, given a -th order, homogeneous and
linear differential operator with constant coefficients, we study a class of
variational problems whose regularizing terms depend on the operator.
Precisely, the regularizers are integrals of spatially inhomogeneous integrands
with convex dependence on the differential operator applied to the image
function. The setting is made rigorous by means of the theory of Radon measures
and of suitable function spaces modeled on . We prove the lower
semicontinuity of the functionals at stake and existence of minimizers for the
corresponding variational problems. Then, we embed the latter into a bilevel
scheme in order to automatically compute the space-dependent regularization
parameters, thus allowing for good flexibility and preservation of details in
the reconstructed image. We establish existence of optima for the scheme and we
finally substantiate its feasibility by numerical examples in image denoising.
The cases that we treat are Huber versions of the first and second order total
variation with both the Huber and the regularization parameter being spatially
dependent. Notably the spatially dependent version of second order total
variation produces high quality reconstructions when compared to
regularizations of similar type, and the introduction of the spatially
dependent Huber parameter leads to a further enhancement of the image details.Comment: 27 pages, 6 figure
Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction
We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based on two distinct sub-networks, with the first sub-network estimating the regularization parameter-map from the input data while the second one unrolling T iterations of the Primal-Dual Three-Operator Splitting (PD3O) algorithm. The latter approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean-corrupted data but crucially without the need of having access to labels for the optimal regularization parameter-maps
Learning Regularization Parameter-Maps for Variational Image Reconstruction Using Deep Neural Networks and Algorithm Unrolling
We introduce a method for the fast estimation of data-adapted, spatially and temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variation (TV) minimization. The proposed approach is inspired by recent developments in algorithm unrolling using deep neural networks (NNs) and relies on two distinct subnetworks. The first subnetwork estimates the regularization parameter-map from the input data. The second subnetwork unrolls
iterations of an iterative algorithm which approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean and corrupted data but crucially without the need for access to labels for the optimal regularization parameter-maps. We first prove consistency of the unrolled scheme by showing that the unrolled minimizing energy functional used for the supervised learning
-converges, as
tends to infinity, to the corresponding functional that incorporates the exact solution map of the TV-minimization problem. Then, we apply and evaluate the proposed method on a variety of large-scale and dynamic imaging problems with retrospectively simulated measurement data for which the automatic computation of such regularization parameters has been so far challenging using the state-of-the-art methods: a 2D dynamic cardiac magnetic resonance imaging (MRI) reconstruction problem, a quantitative brain MRI reconstruction problem, a low-dose computed tomography problem, and a dynamic image denoising problem. The proposed method consistently improves the TV reconstructions using scalar regularization parameters, and the obtained regularization parameter-maps adapt well to imaging problems and data by leading to the preservation of detailed features. Although the choice of the regularization parameter-maps is data-driven and based on NNs, the subsequent reconstruction algorithm is interpretable since it inherits the properties (e.g., convergence guarantees) of the iterative reconstruction method from which the network is implicitly defined
Total Directional Variation for Video Denoising
In this paper, we propose a variational approach for video denoising, based
on a total directional variation (TDV) regulariser proposed in Parisotto et al.
(2018), for image denoising and interpolation. In the TDV regulariser, the
underlying image structure is encoded by means of weighted derivatives so as to
enhance the anisotropic structures in images, e.g. stripes or curves with a
dominant local directionality. For the extension of TDV to video denoising, the
space-time structure is captured by the volumetric structure tensor guiding the
smoothing process. We discuss this and present our whole video denoising
work-flow. Our numerical results are compared with some state-of-the-art video
denoising methods.SP acknowledges UK EPSRC grant EP/L016516/1 for the CCA DTC. CBS acknowledges support from Leverhulme Trust project on Breaking the non-convexity barrier, EPSRC grant Nr. EP/M00483X/1, the EPSRC Centre
EP/N014588/1, the RISE projects CHiPS and NoMADS, the CCIMI and the Alan Turing Institute
A combined first and second order variational approach for image reconstruction
In this paper we study a variational problem in the space of functions of
bounded Hessian. Our model constitutes a straightforward higher-order extension
of the well known ROF functional (total variation minimisation) to which we add
a non-smooth second order regulariser. It combines convex functions of the
total variation and the total variation of the first derivatives. In what
follows, we prove existence and uniqueness of minimisers of the combined model
and present the numerical solution of the corresponding discretised problem by
employing the split Bregman method. The paper is furnished with applications of
our model to image denoising, deblurring as well as image inpainting. The
obtained numerical results are compared with results obtained from total
generalised variation (TGV), infimal convolution and Euler's elastica, three
other state of the art higher-order models. The numerical discussion confirms
that the proposed higher-order model competes with models of its kind in
avoiding the creation of undesirable artifacts and blocky-like structures in
the reconstructed images -- a known disadvantage of the ROF model -- while
being simple and efficiently numerically solvable.Comment: 34 pages, 89 figure
Bilevel Parameter Learning for Higher-Order Total Variation Regularisation Models.
We consider a bilevel optimisation approach for parameter learning in higher-order total variation image reconstruction models. Apart from the least squares cost functional, naturally used in bilevel learning, we propose and analyse an alternative cost based on a Huber-regularised TV seminorm. Differentiability properties of the solution operator are verified and a first-order optimality system is derived. Based on the adjoint information, a combined quasi-Newton/semismooth Newton algorithm is proposed for the numerical solution of the bilevel problems. Numerical experiments are carried out to show the suitability of our approach and the improved performance of the new cost functional. Thanks to the bilevel optimisation framework, also a detailed comparison between TGV 2 and ICTV is carried out, showing the advantages and shortcomings of both regularisers, depending on the structure of the processed images and their noise level.King Abdullah University of Science and Technology (KAUST) (Grant ID: KUKI1-007-43), Engineering and Physical Sciences Research Council (Grant IDs: Nr. EP/J009539/1 âSparse & Higher-order Image Restorationâ and Nr. EP/M00483X/1 âEfficient computational tools for inverse imaging problemsâ), Escuela PolitĂ©cnica Nacional de Quito (Grant ID: PIS 12-14, MATHAmSud project SOCDE âSparse Optimal Control of Differential Equationsâ), Leverhulme Trust (project on âBreaking the non-convexity barrierâ), SENESCYT (Ecuadorian Ministry of Higher Education, Science, Technology and Innovation) (Prometeo Fellowship)This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s10851-016-0662-