Scattering of light due to the presence of aerosol particles along the path of radiation causes atmospheric haze in images. This scattering is significantly less severe in longer wavelength bands than in shorter ones, thus the importance of near-infrared (NIR) information for dehazing color images. This paper first presents an adaptive hyperspectral algorithm that analyzes intensity inconsistencies across spectral bands. It then leverages the algorithm's results to preserve photorealism of the visible color image during the dehazing. The color images are dehazed through a hyperspectral fusion of color and NIR images, taking into account any inconsistencies that can affect the photorealism. Our dehazing results on real images contain no halo or aliasing artifacts in hazy regions and successfully preserve the color image elsewhere.

The satellite-based Global Positioning System (GPS) provides robust localization on smartphones outdoors. In indoor environments, however, no system is close to achieving a similar level of ubiquity, with existing solutions offering different trade-offs in terms of accuracy, robustness and cost. In this paper, we develop a multi-modal positioning system, targeted at smartphones, which aims to get the best out of each of its constituent modalities. More precisely, we combine Bluetooth low energy (BLE) beacons, round-trip-time (RTT) enabled WiFi access points and the smartphone's inertial measurement unit (IMU) to provide a cheap robust localization system that, unlike fingerprinting methods, requires no pre-training. To do this, we use a probabilistic algorithm based on a conditional random field (CRF). We show how to incorporate sparse visual information to improve the accuracy of our system, using pose estimation from pre-scanned visual landmarks, to calibrate the system online. Our method achieves an accuracy of around 2 meters on two realistic datasets, outperforming other distance-based localization approaches. We also compare our approach with an ultra-wideband (UWB) system. While we do not match the performance of UWB, our system is cheap, smartphone compatible and provides satisfactory performance for many applications.

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Knowledge of lens specifications is important to identify the best lens for a given capture scenario and application. Lens manufacturers provide many specifications in their data sheets, and multiple initiatives for testing and comparing different lenses can be found online. However, due to the lack of a suitable metric or technique, no evaluation of axial chromatic aberration is available. In this paper, we propose a metric, Axial Aberration Magnitude or AAM, that assesses the degree of axial chromatic aberration of a given lens. Our metric is generalizable to multispectral acquisition systems and is very simple and cheap to compute. We present the entire procedure and algorithm for computing the AAM metric, and evaluate it for two spectral systems and two consumer lenses.

Range-only localization has applications as diverse as underwater navigation, drone tracking and indoor localization. While the theoretical foundations of lateration-range-only localization for static points-are well understood, there is a lack of understanding when it comes to localizing a moving device. As most interesting applications in robotics involve moving objects, we study the theory of trajectory recovery. This problem has received a lot of attention; however, state-of-the-art methods are of a probabilistic or heuristic nature and not well suited for guaranteeing trajectory recovery. In this letter, we pose trajectory recovery as a quadratic problem and show that we can relax it to a linear form, which admits a closed-form solution. We provide necessary and sufficient recovery conditions and in particular show that trajectory recovery can be guaranteed when the number of measurements is proportional to the trajectory complexity. Finally, we apply our reconstruction algorithm to simulated and real-world data.