arXiv subjects

Michael Hirsch

Publications and source records attributed to Michael Hirsch.

At least 19 recordsLinked to original sources

arXiv2023

The case for studying other planetary magnetospheres and atmospheres in Heliophysics

Heliophysics is the field that "studies the nature of the Sun, and how it influences the very nature of space - and, in turn, the atmospheres of planetary bodies and the technology that exists there." However, NASA's Heliophysics Division tends to limit study of planetary magnetospheres and atmospheres to only those of Earth. This leaves exploration and understanding of space plasma physics at other worlds to the purview of the Planetary Science and Astrophysics Divisions. This is detrimental to the study of space plasma physics in general since, although some cross-divisional funding opportunities do exist, vital elements of space plasma physics can be best addressed by extending the expertise of Heliophysics scientists to other stellar and planetary magnetospheres. However, the diverse worlds within the solar system provide crucial environmental conditions that are not replicated at Earth but can provide deep insight into fundamental space plasma physics processes. Studying planetary systems with Heliophysics objectives, comprehensive instrumentation, and new grant opportunities for analysis and modeling would enable a novel understanding of fundamental and universal processes of space plasma physics. As such, the Heliophysics community should be prepared to consider, prioritize, and fund dedicated Heliophysics efforts to planetary targets to specifically study space physics and aeronomy objectives.

astro-ph.IM↗

arXiv2022

The State of Fortran

A community of developers has formed to modernize the Fortran ecosystem. In this article, we describe the high-level features of Fortran that continue to make it a good choice for scientists and engineers in the 21st century. Ongoing efforts include the development of a Fortran standard library and package manager, the fostering of a friendly and welcoming online community, improved compiler support, and language feature development. The lessons learned are common across contemporary programming languages and help reduce the learning curve and increase adoption of Fortran.

cs.PL↗

arXiv2018

The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution

While implicit generative models such as GANs have shown impressive results in high quality image reconstruction and manipulation using a combination of various losses, we consider a simpler approach leading to surprisingly strong results. We show that texture loss alone allows the generation of perceptually high quality images. We provide a better understanding of texture constraining mechanism and develop a novel semantically guided texture constraining method for further improvement. Using a recently developed perceptual metric employing "deep features" and termed LPIPS, the method obtains state-of-the-art results. Moreover, we show that a texture representation of those deep features better capture the perceptual quality of an image than the original deep features. Using texture information, off-the-shelf deep classification networks (without training) perform as well as the best performing (tuned and calibrated) LPIPS metrics. The code is publicly available.

cs.CV↗

arXiv2018

Perceptual Video Super Resolution with Enhanced Temporal Consistency

With the advent of perceptual loss functions, new possibilities in super-resolution have emerged, and we currently have models that successfully generate near-photorealistic high-resolution images from their low-resolution observations. Up to now, however, such approaches have been exclusively limited to single image super-resolution. The application of perceptual loss functions on video processing still entails several challenges, mostly related to the lack of temporal consistency of the generated images, i.e., flickering artifacts. In this work, we present a novel adversarial recurrent network for video upscaling that is able to produce realistic textures in a temporally consistent way. The proposed architecture naturally leverages information from previous frames due to its recurrent architecture, i.e. the input to the generator is composed of the low-resolution image and, additionally, the warped output of the network at the previous step. Together with a video discriminator, we also propose additional loss functions to further reinforce temporal consistency in the generated sequences. The experimental validation of our algorithm shows the effectiveness of our approach which obtains images with high perceptual quality and improved temporal consistency.

cs.CV↗

arXiv2018

Automatic Estimation of Modulation Transfer Functions

The modulation transfer function (MTF) is widely used to characterise the performance of optical systems. Measuring it is costly and it is thus rarely available for a given lens specimen. Instead, MTFs based on simulations or, at best, MTFs measured on other specimens of the same lens are used. Fortunately, images recorded through an optical system contain ample information about its MTF, only that it is confounded with the statistics of the images. This work presents a method to estimate the MTF of camera lens systems directly from photographs, without the need for expensive equipment. We use a custom grid display to accurately measure the point response of lenses to acquire ground truth training data. We then use the same lenses to record natural images and employ a data-driven supervised learning approach using a convolutional neural network to estimate the MTF on small image patches, aggregating the information into MTF charts over the entire field of view. It generalises to unseen lenses and can be applied for single photographs, with the performance improving if multiple photographs are available.

cs.CV↗

arXiv2017

Learning Blind Motion Deblurring

As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime. However, taking a quick shot frequently yields a blurry result due to unwanted camera shake during recording or moving objects in the scene. Removing these artifacts from the blurry recordings is a highly ill-posed problem as neither the sharp image nor the motion blur kernel is known. Propagating information between multiple consecutive blurry observations can help restore the desired sharp image or video. Solutions for blind deconvolution based on neural networks rely on a massive amount of ground-truth data which is hard to acquire. In this work, we propose an efficient approach to produce a significant amount of realistic training data and introduce a novel recurrent network architecture to deblur frames taking temporal information into account, which can efficiently handle arbitrary spatial and temporal input sizes. We demonstrate the versatility of our approach in a comprehensive comparison on a number of challening real-world examples.

cs.CV↗

arXiv2017

Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.

cs.CV↗

arXiv2017

Online Video Deblurring via Dynamic Temporal Blending Network

State-of-the-art video deblurring methods are capable of removing non-uniform blur caused by unwanted camera shake and/or object motion in dynamic scenes. However, most existing methods are based on batch processing and thus need access to all recorded frames, rendering them computationally demanding and time consuming and thus limiting their practical use. In contrast, we propose an online (sequential) video deblurring method based on a spatio-temporal recurrent network that allows for real-time performance. In particular, we introduce a novel architecture which extends the receptive field while keeping the overall size of the network small to enable fast execution. In doing so, our network is able to remove even large blur caused by strong camera shake and/or fast moving objects. Furthermore, we propose a novel network layer that enforces temporal consistency between consecutive frames by dynamic temporal blending which compares and adaptively (at test time) shares features obtained at different time steps. We show the superiority of the proposed method in an extensive experimental evaluation.

cs.CV↗

arXiv2017

Discriminative Transfer Learning for General Image Restoration

Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency. However, these methods require separate training for each restoration task (e.g., denoising, deblurring, demosaicing) and problem condition (e.g., noise level of input images). This makes it time-consuming and difficult to encompass all tasks and conditions during training. In this paper, we propose a discriminative transfer learning method that incorporates formal proximal optimization and discriminative learning for general image restoration. The method requires a single-pass training and allows for reuse across various problems and conditions while achieving an efficiency comparable to previous discriminative approaches. Furthermore, after being trained, our model can be easily transferred to new likelihood terms to solve untrained tasks, or be combined with existing priors to further improve image restoration quality.

cs.CV↗

arXiv2016

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input. Traditionally, the performance of algorithms for this task is measured using pixel-wise reconstruction measures such as peak signal-to-noise ratio (PSNR) which have been shown to correlate poorly with the human perception of image quality. As a result, algorithms minimizing these metrics tend to produce over-smoothed images that lack high-frequency textures and do not look natural despite yielding high PSNR values. We propose a novel application of automated texture synthesis in combination with a perceptual loss focusing on creating realistic textures rather than optimizing for a pixel-accurate reproduction of ground truth images during training. By using feed-forward fully convolutional neural networks in an adversarial training setting, we achieve a significant boost in image quality at high magnification ratios. Extensive experiments on a number of datasets show the effectiveness of our approach, yielding state-of-the-art results in both quantitative and qualitative benchmarks.

cs.CV↗

arXiv2016

Depth Estimation Through a Generative Model of Light Field Synthesis

Light field photography captures rich structural information that may facilitate a number of traditional image processing and computer vision tasks. A crucial ingredient in such endeavors is accurate depth recovery. We present a novel framework that allows the recovery of a high quality continuous depth map from light field data. To this end we propose a generative model of a light field that is fully parametrized by its corresponding depth map. The model allows for the integration of powerful regularization techniques such as a non-local means prior, facilitating accurate depth map estimation.

cs.CV↗

arXiv2016

End-to-End Learning for Image Burst Deblurring

We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network architecture. Our proposed hybrid-architecture combines the explicit prediction of a deconvolution filter and non-trivial averaging of Fourier coefficients in the frequency domain. In order to make full use of the information contained in all images in one burst, the proposed network embeds smaller networks, which explicitly allow the model to transfer information between images in early layers. Our system is trained end-to-end using standard backpropagation on a set of artificially generated training examples, enabling competitive performance in multi-frame blind deconvolution, both with respect to quality and runtime.

cs.CV↗

arXiv2015

Reconstruction of Fine Scale Auroral Dynamics

We present a feasibility study for a high frame rate, short baseline auroral tomographic imaging system useful for estimating parametric variations in the precipitating electron number flux spectrum of dynamic auroral events. Of particular interest are auroral substorms, characterized by spatial variations of order 100 m and temporal variations of order 10 ms. These scales are thought to be produced by dispersive Alfv\'en waves in the near-Earth magnetosphere. The auroral tomography system characterized in this paper reconstructs the auroral volume emission rate to estimate the characteristic energy and location in the direction perpendicular to the geomagnetic field of peak electron precipitation flux using a distributed network of precisely synchronized ground-based cameras. As the observing baseline decreases, the tomographic inverse problem becomes highly ill-conditioned; as the sampling rate increases, the signal-to-noise ratio degrades and synchronization requirements become increasingly critical. Our approach to these challenges uses a physics-based auroral model to regularize the poorly-observed vertical dimension. Specifically, the vertical dimension is expanded in a low-dimensional basis consisting of eigenprofiles computed over the range of expected energies in the precipitating electron flux, while the horizontal dimension retains a standard orthogonal pixel basis. Simulation results show typical characteristic energy estimation error less than 30% for a 3 km baseline achievable within the confines of the Poker Flat Research Range, using GPS-synchronized Electron Multiplying CCD cameras with broad-band BG3 optical filters that pass prompt auroral emissions.

physics.space-ph↗

arXiv2014

Objektorientierte Graphendarstellung von Simulink-Modellen zur einfachen Analyse und Transformation

In software and hardware development MATLAB and Simulink are used to model cyber physical systems for many years, , especially in automation technology and the automotive industry. Compliance with the required product quality and project efficiency is facilitated by analyzing and transforming Simulink models. The existing API, provided by MATLAB is only suitable for programmatic changing of Simulink models. We show using our own tool which is used in industry, how such as a Simulink model can be edited more easily. For this purpose the model, is converted to an object-oriented class structure that provides convenient access and editing APIs and allows applying well-known algorithms and analyses from graph theory directly. It is also designed as a bi-directional tool, so it transforms a Simulink model into a graph representation and vice versa. ----- In der Software- und Hardwareentwicklung wird seit Jahren verstärkt MATLAB und Simulink für die Modellierung von cyberphysikalischen Systemen, insbesondere in der Automatisierungstechnik und der Automobilindustrie eingesetzt. Die Einhaltung der notwendigen Produktqualität und Projekteffizienz wird durch Analysen und Transformationen auf Simulink-Modellen erleichtert. Die bestehende, von MATLAB bereitgestellte, API ist für die programmatische Veränderung von Simulink-Modellen nur bedingt geeignet. Wir zeigen deshalb anhand eines eigenen, im industriellen Einsatz befindlichen Werkzeugs, wie ein Simulink-Modell leichter bearbeitet werden kann. Dazu wird es in eine objektorientierte Klassenstruktur überführt, die einen komfortablen Zugang und Bearbeitungs-APIs bietet und es erlaubt bekannte Algorithmen und Analysen aus der Graphentheorie direkt anzuwenden. Das Werkzeug ist bidirektional entworfen, es transformiert also ein Simulink-Modell in eine Graphenrepresentation und umgekehrt.

cs.SE↗

arXiv2014

Learning to Deblur

We describe a learning-based approach to blind image deconvolution. It uses a deep layered architecture, parts of which are borrowed from recent work on neural network learning, and parts of which incorporate computations that are specific to image deconvolution. The system is trained end-to-end on a set of artificially generated training examples, enabling competitive performance in blind deconvolution, both with respect to quality and runtime.

cs.CV↗

arXiv2013

Maximizing Kepler science return per telemetered pixel: Detailed models of the focal plane in the two-wheel era

Kepler's immense photometric precision to date was maintained through satellite stability and precise pointing. In this white paper, we argue that image modeling--fitting the Kepler-downlinked raw pixel data--can vastly improve the precision of Kepler in pointing-degraded two-wheel mode. We argue that a non-trivial modeling effort may permit continuance of photometry at 10-ppm-level precision. We demonstrate some baby steps towards precise models in both data-driven (flexible) and physics-driven (interpretably parameterized) modes. We demonstrate that the expected drift or jitter in positions in the two-weel era will help with constraining calibration parameters. In particular, we show that we can infer the device flat-field at higher than pixel resolution; that is, we can infer pixel-to-pixel variations in intra-pixel sensitivity. These results are relevant to almost any scientific goal for the repurposed mission; image modeling ought to be a part of any two-wheel repurpose for the satellite. We make other recommendations for Kepler operations, but fundamentally advocate that the project stick with its core mission of finding and characterizing Earth analogs. [abridged]

astro-ph.IM↗

arXiv2013

Maximizing Kepler science return per telemetered pixel: Searching the habitable zones of the brightest stars

In today's mailing, Hogg et al. propose image modeling techniques to maintain 10-ppm-level precision photometry in Kepler data with only two working reaction wheels. While these results are relevant to many scientific goals for the repurposed mission, all modeling efforts so far have used a toy model of the Kepler telescope. Because the two-wheel performance of Kepler remains to be determined, we advocate for the consideration of an alternate strategy for a >1 year program that maximizes the science return from the "low-torque" fields across the ecliptic plane. Assuming we can reach the precision of the original Kepler mission, we expect to detect 800 new planet candidates in the first year of such a mission. Our proposed strategy has benefits for transit timing variation and transit duration variation studies, especially when considered in concert with the future TESS mission. We also expect to help address the first key science goal of Kepler: the frequency of planets in the habitable zone as a function of spectral type.

astro-ph.IM↗

arXiv2013

S\'{e}rsic galaxy models in weak lensing shape measurement: model bias, noise bias and their interaction

Cosmic shear is a powerful probe of cosmological parameters, but its potential can be fully utilised only if galaxy shapes are measured with great accuracy. Two major effects have been identified which are likely to account for most of the bias for maximum likelihood methods in recent shear measurement challenges. Model bias occurs when the true galaxy shape is not well represented by the fitted model. Noise bias occurs due to the non-linear relationship between image pixels and galaxy shape. In this paper we investigate the potential interplay between these two effects when an imperfect model is used in the presence of high noise. We present analytical expressions for this bias, which depends on the residual difference between the model and real data. They can lead to biases not accounted for in previous calibration schemes. By measuring the model bias, noise bias and their interaction, we provide a complete statistical framework for measuring galaxy shapes with model fitting methods from GRavitational lEnsing Accuracy Testing (GREAT) like images. We demonstrate the noise and model interaction bias using a simple toy model, which indicates that this effect can potentially be significant. Using real galaxy images from the Cosmological Evolution Survey (COSMOS) we quantify the strength of the model bias, noise bias and their interaction. We find that the interaction term is often a similar size to the model bias term, and is smaller than the requirements of the current and shortly upcoming galaxy surveys.

astro-ph.CO↗