Self-Supervised Poisson-Gaussian Denoising.

We extend the blindspot model for self-supervised denoising to handle Poisson-Gaussian noise and introduce an improved training scheme that avoids hyperparameters and adapts the denoiser to the test data. Self-supervised models for denoising learn to denoise from only noisy data and do not require corresponding clean images, which are difficult or impossible to acquire in some application areas of interest such as low-light microscopy. We introduce a new training strategy to handle Poisson-Gaussian noise which is the standard noise model for microscope images.

Evaluation of MRI Denoising Methods Using Unsupervised Learning.

In this paper we evaluate two unsupervised approaches to denoise Magnetic Resonance Images (MRI) in the complex image space using the raw information that k-space holds. The first method is based on Stein's Unbiased Risk Estimator, while the second approach is based on a blindspot network, which limits the network's receptive field. Both methods are tested on two different datasets, one containing real knee MRI and the other consists of synthetic brain MRI.

Fluorescence microscopy datasets for training deep neural networks.

Background: Fluorescence microscopy is an important technique in many areas of biological research. Two factors that limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging and, when imaging live cells, phototoxicity caused by light exposure. Recently developed methods in machine learning are able to greatly improve the signal-to-noise ratio of acquired images.

Instant outdoor localization and SLAM initialization from 2.5D maps.

We present a method for large-scale geo-localization and global tracking of mobile devices in urban outdoor environments. In contrast to existing methods, we instantaneously initialize and globally register a SLAM map by localizing the first keyframe with respect to widely available untextured 2.5D maps.

Model Estimation and Selection towards Unconstrained Real-Time Tracking and Mapping.

We present an approach and prototype implementation to initialization-free real-time tracking and mapping that supports any type of camera motion in 3D environments, that is, parallax-inducing as well as rotation-only motions. Our approach effectively behaves like a keyframe-based Simultaneous Localization and Mapping system or a panorama tracking and mapping system, depending on the camera movement. It seamlessly switches between the two modes and is thus able to track and map through arbitrary sequences of parallax-inducing and rotation-only camera movements.

Global localization from monocular SLAM on a mobile phone.

We propose the combination of a keyframe-based monocular SLAM system and a global localization method. The SLAM system runs locally on a camera-equipped mobile client and provides continuous, relative 6DoF pose estimation as well as keyframe images with computed camera locations. As the local map expands, a server process localizes the keyframes with a pre-made, globally-registered map and returns the global registration correction to the mobile client.

Claiming power through hardship: Initiation narratives of Palestinian traditional women healers in Israel.

This article offers a new perspective regarding the initiation of traditional healers through an analysis of the initiation narratives of ten Muslim Palestinian traditional women healers in Israel. The analysis points to three shared themes within these narratives: they begin with a description of the initiation's source (inheritance or revelation); they focus primarily on a later stage of the woman healer's life; and they include an in-depth description of the suffering and hardships that she has endured. These findings describe the initiation of Palestinian traditional women healers in Israel as a process rather than an event; as a derivative of the woman healer's life rather than its driving force.