Explore the vast range of titles available.

Mobile phone cameras are often significantly more useful than professional digital single-lens reflex (DSLR) cameras. Knowledge of the camera spectral sensitivity function is important in many fields that make use of images. In this study, methods for measuring and estimating spectral sensitivity functions for mobile phone cameras are developed. In the direct measurement method, the spectral sensitivity at each wavelength is measured using monochromatic light. Although accurate, this method is time-consuming and expensive. The indirect estimation method is based on color samples, in which the spectral sensitivities are estimated from the input data of color samples and the corresponding output RGB values from the camera. We first present an imaging system for direct measurements. A variety of mobile phone cameras are measured using the system to create a database of spectral sensitivity functions. The features of the measured spectral sensitivity functions are then studied using principal component analysis (PCA) and the statistical features of the spectral functions extracted. We next describe a normal method to estimate the spectral sensitivity functions using color samples and point out some drawbacks of the method. A method to solve the estimation problem using the spectral features of the sensitivity functions in addition to the color samples is then proposed. The estimation is stable even when only a small number of spectral features are selected. Finally, the results of the experiments to confirm the feasibility of the proposed method are presented. We establish that our method is excellent in terms of both the data volume of color samples required and the estimation accuracy of the spectral sensitivity functions.

With the emergence of huge volumes of high-resolution Hyperspectral Images (HSI) produced by different types of imaging sensors, analyzing and retrieving these images require effective image description and quantification techniques. Compared to remote sensing RGB images, HSI data contain hundreds of spectral bands (varying from the visible to the infrared ranges) allowing profile materials and organisms that only hyperspectral sensors can provide. In this article, we study the importance of spectral sensitivity functions in constructing discriminative representation of hyperspectral images. The main goal of such representation is to improve image content recognition by focusing the processing on only the most relevant spectral channels. The underlying hypothesis is that for a given category, the content of each image is better extracted through a specific set of spectral sensitivity functions. Those spectral sensitivity functions are evaluated in a Content-Based Image Retrieval (CBIR) framework. In this work, we propose a new HSI dataset for the remote sensing community, specifically designed for Hyperspectral remote sensing retrieval and classification. Exhaustive experiments have been conducted on this dataset and on a literature dataset. Obtained retrieval results prove that the physical measurements and optical properties of the scene contained in the HSI contribute in an accurate image content description than the information provided by the RGB image presentation.

Photometric camera calibration is often required in physics-based computer vision. There have been a number of studies to estimate camera response functions (gamma function), and vignetting effect from images. However less attention has been paid to camera spectral sensitivities and white balance settings. This is unfortunate, since those two properties significantly affect image colors. Motivated by this, a method to estimate camera spectral sensitivities and white balance setting jointly from images with sky regions is introduced. The basic idea is to use the sky regions to infer the sky spectra. Given sky images as the input and assuming the sun direction with respect to the camera viewing direction can be extracted, the proposed method estimates the turbidity of the sky by fitting the image intensities to a sky model. Subsequently, it calculates the sky spectra from the estimated turbidity. Having the sky RGB values and their corresponding spectra, the method estimates the camera spectral sensitivities together with the white balance setting. Precomputed basis functions of camera spectral sensitivities are used in the method for robust estimation. The whole method is novel and practical since, unlike existing methods, it uses sky images without additional hardware, assuming the geolocation of the captured sky is known. Experimental results using various real images show the effectiveness of the method.

Although β-CsPbI₃ has a bandgap favorable for application in tandem solar cells, depositing and stabilizing β-CsPbI₃ experimentally has remained a challenge.We obtained highly crystalline β-CsPbI₃ films with an extended spectral response and enhanced phase stability. Synchrotron-based x-ray scattering revealed the presence of highly oriented β-CsPbI₃ grains, and sensitive elemental analyses—including inductively coupled plasma mass spectrometry and time-of-flight secondary ion mass spectrometry—confirmed their all-inorganic composition. We further mitigated the effects of cracks and pinholes in the perovskite layer by surface treating with choline iodide, which increased the charge-carrier lifetime and improved the energy-level alignment between the β-CsPbI₃ absorber layer and carrier-selective contacts. The perovskite solar cells made from the treated material have highly reproducible and stable efficiencies reaching 18.4% under 45 ± 5°C ambient conditions.

Spectrometers with ever-smaller footprints are sought after for a wide range of applications in which minimized size and weight are paramount, including emerging in situ characterization techniques. We report on an ultracompact microspectrometer design based on a single compositionally engineered nanowire. This platform is independent of the complex optical components or cavities that tend to constrain further miniaturization of current systems. We show that incident spectra can be computationally reconstructed from the different spectral response functions and measured photocurrents along the length of the nanowire. Our devices are capable of accurate, visible-range monochromatic and broadband light reconstruction, as well as spectral imaging from centimeter-scale focal planes down to lensless, single-cell–scale in situ mapping.

Remote sensing has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. However, the most appropriate spatial resolution necessary to detect diversity remains unclear. At coarse resolution, differences among spectral patterns may be too weak to detect. In contrast, at fine resolution, redundant information may be introduced. To explore the effect of spatial resolution, we studied the scale dependence of spectral diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Our study involved a scaling exercise comparing synthetic pixels resampled from high-resolution images within manipulated diversity treatments. Hyperspectral data were collected using several instruments on both ground and airborne platforms. We used the coefficient of variation (CV) of spectral reflectance in space as the indicator of spectral diversity and then compared CV at different scales ranging from 1 mm² to 1 m² to conventional biodiversity metrics, including species richness, Shannon’s index, Simpson’s index, phylogenetic species variation, and phylogenetic species evenness. In this study, higher species richness plots generally had higher CV. CV showed higher correlations with Shannon’s index and Simpson’s index than did species richness alone, indicating evenness contributed to the spectral diversity. Correlations with species richness and Simpson’s index were generally higher than with phylogenetic species variation and evenness measured at comparable spatial scales, indicating weaker relationships between spectral diversity and phylogenetic diversity metrics than with species diversity metrics. High resolution imaging spectrometer data (1 mm² pixels) showed the highest sensitivity to diversity level. With decreasing spatial resolution, the difference in CV between diversity levels decreased and greatly reduced the optical detectability of biodiversity. The optimal pixel size for distinguishing α diversity in these prairie plots appeared to be around 1 mm to 10 cm, a spatial scale similar to the size of an individual herbaceous plant. These results indicate a strong scale-dependence of the spectral diversity-biodiversity relationships, with spectral diversity best able to detect a combination of species richness and evenness, and more weakly detecting phylogenetic diversity. These findings can be used to guide airborne studies of biodiversity and develop more effective large-scale biodiversity sampling methods.

We present an estimate of the performance that will be achieved during on-orbit operations of the JWST mid-infrared instrument, MIRI. The efficiency of the main imager and spectrometer systems in detecting photons from an astronomical target are presented, based on measurements at subsystem and instrument-level testing, with the end-to-end transmission budget discussed in some detail. The brightest target fluxes that can be measured without saturating the detectors are provided. The sensitivity for long-duration observations of faint sources is presented in terms of the target flux required to achieve a signal-to-noise ratio of 10 after a 10,000 s observation. The algorithms used in the sensitivity model are presented, including the understanding gained during testing of the MIRI flight model and flight-like detectors.

The efficiency of perovskite solar cells has surged in the past few years, while the bandgaps of current perovskite materials for record efficiencies are much larger than the optimal value, which makes the efficiency far lower than the Shockley–Queisser efficiency limit. Here we show that utilizing the below-bandgap absorption of perovskite single crystals can narrow down their effective optical bandgap without changing the composition. Thin methylammonium lead triiodide single crystals with tuned thickness of tens of micrometers are directly grown on hole-transport-layer covered substrates by a hydrophobic interface confined lateral crystal growth method. The spectral response of the methylammonium lead triiodide single crystal solar cells is extended to 820 nm, 20 nm broader than the corresponding polycrystalline thin-film solar cells. The open-circuit voltage and fill factor are not sacrificed, resulting in an efficiency of 17.8% for single crystal perovskite solar cells. Thin films of halide perovskites are promising for solar cell technology but they do not perform well at the band edge due to the low optical absorption. Herein, Chen et al. fabricate a high efficiency single crystal perovskite solar cell with thicker single crystals to harvest the below-bandgap photons.

Van der Waals (vdW) interfaces based on 2D materials are promising for optoelectronics, as interlayer transitions between different compounds allow tailoring of the spectral response over a broad range. However, issues such as lattice mismatch or a small misalignment of the constituent layers can drastically suppress electron–photon coupling for these interlayer transitions. Here, we engineered type-II interfaces by assembling atomically thin crystals that have the bottom of the conduction band and the top of the valence band at the Γ point, and thus avoid any momentum mismatch. We found that these van der Waals interfaces exhibit radiative optical transitions irrespective of the lattice constant, the rotational and/or translational alignment of the two layers or whether the constituent materials are direct or indirect gap semiconductors. Being robust and of general validity, our results broaden the scope of future optoelectronics device applications based on two-dimensional materials. Type-II van der Waals interfaces formed by different two-dimensional materials enable robust interlayer optical transitions, regardless of common issues such as lattice constant mismatch, layer misalignment or whether the constituent compounds are direct or indirect band semiconductors.

Dual-comb spectroscopy (DCS) offers high sensitivity and wide spectral coverage without the need for bulky spectrometers or mechanical moving parts. And DCS in the mid-infrared (mid-IR) is of keen interest because of inherently strong molecular spectroscopic signatures in these bands. We report GHz-resolution mid-IR DCS of methane and ethane that is derived from counter-propagating (CP) soliton microcombs in combination with interleaved difference frequency generation. Because all four combs required to generate the two mid-IR combs rely upon stability derived from a single high-Q microcavity, the system architecture is both simplified and does not require external frequency locking. Methane and ethane spectra are measured over intervals as short as 0.5 ms, a time scale that can be further reduced using a different CP soliton arrangement. Also, tuning of spectral resolution on demand is demonstrated. Although at an early phase of development, the results are a step towards mid-IR gas sensors with chip-based architectures for chemical threat detection, breath analysis, combustion studies, and outdoor observation of trace gases. Chip-based architectures for mid-infrared gas sensing could enable many applications. In this direction, the authors demonstrate a microcomb-based dual-comb spectroscopy sensor with GHz resolution in the mid-IR band, with stability completely determined by a single high-Q microresonator.