Wide Field Of View Research Articles

Super-Resolution of GF-1 Multispectral Wide Field of View Images via a Very Deep Residual Coordinate Attention Network

GF-1 multispectral wide field of view (WFV) images, with a spatial resolution of 16 m, have been widely used in earth monitoring. However, the spatial details provided by WFV images are not sufficient for many applications. Thus, this letter proposes a novel WFV image super-resolution (SR) algorithm called GFRCAN based on a very deep residual coordinate attention network. To form a very deep network, the residual-in-residual (RIR) structure consisting of several residual groups (RG) with long skip connections is used. Meanwhile, the residual coordinate attention block (RCOAB) and adaptive multi-scale spatial attention module (AMSA) are incorporated to focus on the high-frequency information and multi-scale features adaptive weighted fusion. Besides, the spectral and spatial details of SR images are improved by incorporating peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) into the loss function. Both subjective and objective evaluation results show that the proposed model outperforms the state-of-the-art methods.

Global Convolutional Neural Networks With Self-Attention for Fisheye Image Rectification

Fisheye images are attracting attention in computer vision such as autonomous vehicles and virtual reality because of the wide field of view (WFoV). However, fisheye images have geometric distortions caused by the refractive index of the lens. Conventional fisheye rectification methods require multiple images to calculate distortion coefficients and lens intrinsic parameters. This means that if the fisheye lens is changed, the same operation will have to be repeated. On the other hand, by using deep learning, images with different distortion coefficients can be rectified. Also, with end-to-end learning, no feature engineering is required. To improve the performance of fisheye image rectification, we propose global convolutional neural networks with self-attention to rectify the fisheye images. The proposed method employs dilated convolutional neural networks (D-CNNs) to enlarge receptive fields, and self-attention to extract the most important features of input images. In this way, the proposed method can extract global features from input images. To better train and evaluate the proposed method, we generate fisheye images from the Place2 dataset with Cartesian and polar coordinates, and label them with original images (ground-truth). we also schedule the learning rate with cosine annealing and use an integrated loss function. The experimental results show that the proposed method achieves an excellent performance in both qualitative and quantitative evaluations.

DCNet: A Deformable Convolutional Cloud Detection Network for Remote Sensing Imagery

Recently, deep convolutional neural networks (CNNs) have made important progress in cloud detection with powerful representation learning capability and yield significant performance. However, most existing CNN-based cloud detection methods still face serious challenges because of the variable geometry of clouds and the complexity of underlying surfaces. It is attributed that they only use the fixed grid to extract contextual information, which lacks internal mechanisms to handle the geometric transformations of clouds. To tackle this problem, we propose a deformable convolutional cloud detection network with an encoder-decoder architecture, named DCNet, which can enhance the adaptability of a model to cloud variations. Specifically, we introduce deformable convolution blocks at the encoder to capture saliency spatial contexts adaptively based on the morphological characteristics of clouds and generate high-level semantic representations. After this, we incorporate skip-connection mechanisms into the decoder that integrate low-level spatial contexts as guidance to recover high-level semantic pixel localization and export precise cloud-detection results. Extensive experiments on the GF-1 wide field-of-view (WFV) Satellite Imagery demonstrate that DCNet outperforms several state-of-the-art methods. A public reference implementation of our proposed model in PyTorch is available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/NiAn-creator/deformableCloudDetection.git</uri>.</i>

Cloud Detection Method Using CNN Based on Cascaded Feature Attention and Channel Attention

Cloud detection is of great significance for the subsequent analysis and application of remote-sensing images, and it is a critical part of remote-sensing image preprocessing. In this article, we propose a cloud detection method using convolutional neural networks based on cascaded feature attention and channel attention (CFCA-Net). The CFCA-Net uses cascaded feature attention module (CFAM) to enhance the attention of the network toward important color feature and texture feature. The CFAM cascaded the color feature attention and texture feature attention module in the encoder. The CFAN-Net also uses channel attention to highlight the important information in the channel dimensions. The attention module is based on multi-scale features and uses dilated convolution with different dilation rates to obtain information about multiple receptive fields. Moreover, a loss function combined quadtree and binary cross-entropy (BCE) was also introduced to make the network focus on the edge of cloud area. We validated our CFCA-Net on the Gaofen-1 wide field-of-view (WFV) imagery dataset. The experimental results show that the CFCA-Net performs well under different scenarios, and its overall accuracy reaches 97.55%. Moreover, subjective cloud detection results also prove the effectiveness of our algorithm.

Design of a real-time fiber-optic infrared imaging system with wide-angle and large depth of field

A key limitation in the observation of instruments used in operations and heart sutures during a procedure is the scattering and absorption during optical imaging in the presence of blood. Therefore, we propose a novel real-time fiber-optic infrared imaging system simultaneously capturing a flexible wide field of view (FOV) and large depth of field infrared image in real time. The assessment criteria for imaging quality of the objective and coupling lens have been optimized and evaluated. Furthermore, the feasibility of manufacturing and assembly has been demonstrated with tolerance sensitivity and the Monte Carlo analysis. The simulated results show that the optical system can achieve a large working distance of 8 to 25 mm, a wide FOV of 120°, and the relative illuminance is over 0.98 in the overall FOV. To achieve high imaging quality in the proposed system, the modulation transfer function is over 0.661 at 16.7 lp/mm for a 320×256 short wavelength infrared camera sensor with a pixel size of 30 µm.

Efficient ArcSAR Focusing in the Wavenumber Domain

Arc synthetic aperture radar (ArcSAR) is a ground-based remote imaging technology with the ability to cover wide fields of view. However, because its azimuth is formed by scanning angle rotation, the focusing of ArcSAR images is different from classical SAR focusing. Since the existing imaging methods cannot give a good balance between computational efficiency and accuracy, the wavenumber domain algorithm (WMA) could become an interesting alternative. Due to the fact that in ArcSAR imaging, the slant range measurement depends on a sine term of the scanning angle, no dedicated WMA-based approach for ArcSAR imaging has been formulated yet. The main challenge in this context is that the solution of the stationary phase point cannot be resolved explicitly via Fourier transform (FT). This article proposes a new wavenumber domain imaging method, which exploits the sine law in the process of solving the stationary phase point during FT along the direction of the received echo using the triangular relationship formed by the target, the radar, and the rotation center of radar and then obtains the exact phase error expression in range and angular wavenumber domain without any approximation of the slant range or scanning angle. Using this formulation, we develop the corresponding phase error compensation method and complete image focusing. Through point target simulation and experiments on real ArcSAR data, the effectiveness of this method is verified in terms of imaging accuracy and computational efficiency.

The First Large Absorption Survey in H i (FLASH): I. Science goals and survey design

Abstract We describe the scientific goals and survey design of the First Large Absorption Survey in H i (FLASH), a wide field survey for 21-cm line absorption in neutral atomic hydrogen (H i) at intermediate cosmological redshifts. FLASH will be carried out with the Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope and is planned to cover the sky south of $\delta \approx +40\,\deg$ at frequencies between 711.5 and 999.5 MHz. At redshifts between $z = 0.4$ and $1.0$ (look-back times of 4 – 8 Gyr), the H i content of the Universe has been poorly explored due to the difficulty of carrying out radio surveys for faint 21-cm line emission and, at ultra-violet wavelengths, space-borne searches for Damped Lyman- $\alpha$ absorption in quasar spectra. The ASKAP wide field of view and large spectral bandwidth, in combination with a radio-quiet site, will enable a search for absorption lines in the radio spectra of bright continuum sources over 80% of the sky. This survey is expected to detect at least several hundred intervening 21-cm absorbers and will produce an H i-absorption-selected catalogue of galaxies rich in cool, star-forming gas, some of which may be concealed from optical surveys. Likewise, at least several hundred associated 21-cm absorbers are expected to be detected within the host galaxies of radio sources at $0.4 &lt; z &lt; 1.0$ , providing valuable kinematical information for models of gas accretion and jet-driven feedback in radio-loud active galactic nuclei. FLASH will also detect OH 18-cm absorbers in diffuse molecular gas, megamaser OH emission, radio recombination lines, and stacked H i emission.

Direction Finding of RFID Tags in UHF Band Using a Passive Beam-Scanning Leaky-Wave Antenna

This work proposes a novel system for location applications of RFID tags in UHF band. The system is based on a frequency-scanning leaky-wave antenna (LWA), commodity hardware and Angle-of-Arrival (AoA) estimation of the signal reflected from RFID tags, by using only RSSI (Received Signal Strength Indicator) data. The LWA is completely passive, manufactured in simple printed technology and directly connected to the RFID reader. The dispersive nature of the LWA combined with the selection of a proper high permittivity substrate allows the radiation of frequency steered directive beams. The system is based on four directive beams created by the two-port antenna and two RFID channels to estimate the AoA by amplitude-monopulse signal processing and angular pseudospectrum technique. Thus, our approach is efficient, simple and low-cost, since the use of expensive or complex hardware required for electronic phased-array steering/beam-switching is avoided and the required computational load is low. Several experiments have been performed in different scenarios, reporting a wide FoV of 130&#x2218;, multi-tag detection and a RMSE of 8,1&#x2218;.

Mutual Coupling and Channel Imbalance Calibration of Colocated MIMO Radars

The Multiple-Input Multiple-Output (MIMO) radar combines the signals from multiple transmit and receive elements separately to form a virtual array. Consequently, system imperfections in the MIMO radar caused by channel imbalance and mutual coupling are repeatedly present in the virtual array, affecting the beamforming performance. In this paper, the beam pattern deviations caused by these imperfections are described mathematically in relation to a single point target. Furthermore, a nonparametric calibration technique to estimate and compensate for these effects at different angles is presented by exploiting that the same imperfections are present multiple times in the virtual array. The proposed calibration method is applied to measurements with an 8 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 8 colocated X-band MIMO radar, and the peak sidelobe level (PSL) and the integrated sidelobe level (ISL) are used as performance parameters. The calibration technique is found to improve both the PSL and the ISL by approximately 15 dB within the whole field-of-view (FOV) for antennas that are not minimum scatterers. For angles above &#x00B1;30&#x00B0; from the antenna boresight, the performance of the proposed calibration method is found to surpass other calibration methods. This is advantageous for MIMO radars as they typically cover a wide FOV.

Wide-angle, monocular head tracking using passive markers

BackgroundCamera images can encode large amounts of visual information of an animal and its environment, enabling high fidelity 3D reconstruction of the animal and its environment using computer vision methods. Most systems, both markerless (e.g. deep learning based) and marker-based, require multiple cameras to track features across multiple points of view to enable such 3D reconstruction. However, such systems can be expensive and are challenging to set up in small animal research apparatuses. New methodsWe present an open-source, marker-based system for tracking the head of a rodent for behavioral research that requires only a single camera with a potentially wide field of view. The system features a lightweight visual target and computer vision algorithms that together enable high-accuracy tracking of the six-degree-of-freedom position and orientation of the animal’s head. The system, which only requires a single camera positioned above the behavioral arena, robustly reconstructs the pose over a wide range of head angles (360° in yaw, and approximately ± 120° in roll and pitch). ResultsExperiments with live animals demonstrate that the system can reliably identify rat head position and orientation. Evaluations using a commercial optical tracker device show that the system achieves accuracy that rivals commercial multi-camera systems. Comparison with existing methodsOur solution significantly improves upon existing monocular marker-based tracking methods, both in accuracy and in allowable range of motion. ConclusionsThe proposed system enables the study of complex behaviors by providing robust, fine-scale measurements of rodent head motions in a wide range of orientations.

Achieving Ultraviolet C and Ultraviolet B Dual‐Band Persistent Luminescence by Manipulating the Garnet Structure

AbstractPersistent luminescence (PersL) at the high‐energy spectral end (ultraviolet, UV) is generally hard to achieve, although a long‐lasting UV PersL can play an irreplaceable role in outdoor imaging and other UV‐triggered applications. No PersL with dual ultraviolet C (UVC) and ultraviolet B (UVB) bands from a single material has previously been attained until the authors have designed Pr3+‐doped (Ca1.5Y1.5)(Al3.5Si1.5)O12 (CYAS‐Pr) through cosubstituting Ca2+–Si4+ into Y3Al5O12 garnet structure in this study. The CYAS‐Pr exhibits lattice shrinkage and distortion caused by the Ca2+–Si4+ cosubstitution, which boosts the creation of trapping centers essential to the unprecedented UVC and UVB dual‐band PersL with maxima at 266 and 311 nm upon being charged by UV irradiation at 254 nm. Both UVC and UVB PersL decay over 12 h with the existence of two different Pr3+ environments at Ca2+ and Y3+ sites, respectively. Moreover, the CYAS‐Pr shows efficient dual information storage and precise long‐distance self‐sustained “solar‐blind” imaging capabilities in a wide field of view. It is expected that this dual UVC and UVB PersL in the CYAS‐Pr will trigger further exploration toward persistent sterilization and persistent photocatalysis along with other applications.

Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.

LAND UNMANNED COMPLEX WITH PASSIVE RANGE MEASUREMENT

The automatic robotic complex will obviously become one of the main subjects in the conduct of military actions in the near future. To control movement parameters, as well as search, target detection and aiming, the complex includes a technical vision system.&#x0D; The minimum sufficient configuration of such a system includes a television search camera with a wide field of view, television and thermal imaging sights, and a rangefinder. The use of laser rangefinders ensures high accuracy of aiming weapons, but generates a powerful unmasking feature. To ensure the secrecy of the functioning of the robotic complex, range finders can operate in a passive mode using information from on-board television cameras. But at the same time, the metrological characteristics of the information measuring channel are significantly deteriorated. Accuracy of five methods of passive distance measurement with application of TV-systems of land unmanned complex is assessed in paper.&#x0D; Classic method of TV-sight external-base range-finder with scale, designed on human height 1,65 m, is ensuring measurement accuracy 135 m on distance 1000 m. External base method, when a range finger scale is forming on remote display as variable length vertical line in process of target framing, is ensuring measurement accuracy 100,3 m on dis-tance 1000 m. Fixed-base range-finder method, when distance between entrance pupils of TV-sight and wide viewing field camera using as base, is ensuring measurement accuracy 76 m on distance 1000 m. &#x0D; Distance measurement method due to displacement of land unmanned complex ensures a measurement accuracy up to 168 m on distance 1000 m.&#x0D; Measurement method due to using zoom-objective is not suitable for land unmanned complex. Proposals have been formulated for the spatial layout of the computer vision system, in which the method of the fixed-base rangefinder is implemented, which ensures the highest measurement accuracy.

Practical review on photoacoustic computed tomography using curved ultrasound array transducer.

Photoacoustic computed tomography (PACT) has become a promising imaging modality from laboratory to clinical research. Of many components of PACT system, the ultrasound (US) array transducer is an essential device to simultaneously receive photoacoustic (PA) signals from several directions in a parallel manner. Many research groups and companies have developed various types of US array transducers while accounting the properties of the PA waves to achieve better image quality, deeper imaging depth, faster imaging speed, and a wider field of view. In this review, we present the implementation and application of the state-of-the-art PACT systems using several types of curved US arrays: arc-shaped, ring-shaped, and hemispherical array transducers. Furthermore, we discuss the current limitations of PACT and also potential future directions for enhancing them.

Photoacoustic Imaging of Brain Functions: Wide Filed‐of‐View Functional Imaging with High Spatiotemporal Resolution

AbstractNeuroimaging techniques can reveal complex brain functions associated with behaviors and emotions. However, most existing neuroimaging methods suffer from balancing tradeoffs among the temporal and spatial resolutions as well as the field of view (FOV). A high spatiotemporal resolution photoacoustic neuroimaging (PANI) technique with a wide FOV sufficient to cover the entire mouse cerebral cortex in transverse plane and measure fast neuronal dynamics is developed. Based on the experimental validations from both drug‐induced epilepsy and electrical stimulation in the mouse brain, PANI features the following capabilities: i) deriving functional information from the fluctuation of total hemoglobin (HbT) to identify the neural activity and connectivity of the mouse cortex; ii) integrating with other neurological recording methods such as electroencephalograph (EEG) to investigate the neurovascular coupling; and iii) revealing the interactive connectivity between distant brain regions under different physiological conditions. The special features of PANI offer a unique window on investigating the fast neural dynamics with a wide FOV at the capillary scale.

Learning edge-preserved image stitching from multi-scale deep homography

Image stitching is a classical and challenging technique in computer vision, which aims to generate an image with a wide field of view. The traditional methods heavily depend on feature detection and require the feature points to be dense and evenly distributed in the image, leading to poor robustness in low-texture scenes. Learning methods are rarely studied due to the unavailability of ground truth stitched results, showing unreliable performance on real-world datasets. In this paper, we propose an image stitching learning framework, which consists of a multi-scale deep homography module and an edge-preserved deformation module. First, we design a multi-scale deep homography module to estimate the accurate homography progressively from coarse to fine. After that, an edge-preserved deformation module is designed to learn the deformation rules of image stitching from edge to content, generating the stitched image with artifacts eliminated. Besides, the proposed supervised learning framework can stitch images of arbitrary resolutions and demonstrate good generalization capability in real-world images. Experiments show that our superiority to the existing homography solutions and image stitching algorithms.

Triple-modality co-registered endoscope featuring wide-field reflectance imaging, and high-resolution multiphoton and optical coherence microscopy.

We present the design and feasibility testing of a multimodal co-registered endoscope based on a dual-path optical system integrated with a scanning piezo. This endoscope incorporates three different imaging modalities. A large field of view reflectance imaging system enables visualization of objects several millimeters in front of the endoscope, while optical coherence microscopy and multiphoton microscopy are employed in contact with tissue to further analyze suspicious areas. The optical system allows multiple different imaging modalities by employing a dual optical path. One path features a low numerical aperture and wide field of view to allow reflectance imaging of distant objects. The other path features a high numerical aperture and short working distance to allow microscopy techniques such as optical coherence microscopy and multiphoton microscopy. Images of test targets were obtained with each imaging modality to verify and characterize the imaging capabilities of the endoscope. The reflectance modality was demonstrated with a 561 nm laser to allow high contrast with blood vessels. It achieved a lateral resolution of 24.8 μm at 5 mm and a working distance from 5 mm to 30 mm. Optical coherence microscopy (OCM) was performed with a 1300 nm super-luminescent diode since this wavelength experiences low relative scattering to allow for deeper tissue imaging. Measured OCM lateral and axial resolution was 4.0 μm and 14.2 μm, respectively. Multiphoton microscopy (MPM) was performed with a custom 1400 nm femtosecond fiber laser, a wavelength suitable for exciting multiple exogenous and some endogenous fluorophores, as well as providing information on tissue composition through harmonic generation processes. A 4.0 μm MPM lateral resolution was measured.

Semi-Automatic Extraction and Mapping of Farmlands Based on High-Resolution Remote Sensing Images

Extraction of agricultural parcels from high-resolution satellite imagery is an important task in precision agriculture. Here, we present a semi-automatic approach for agricultural parcel detection that achieves high accuracy and efficiency. Unlike the techniques presented in previous literatures, this method is pixel based, and it exploits the properties of a spectral angle mapper (SAM) to develop customized operators to accurately derive the parcels. The main steps of the method are sample selection, textural analysis, spectral homogenization, SAM, thresholding, and region growth. We have systematically evaluated the algorithm proposed on a variety of images from Gaofen-1 wide field of view (GF-1 WFV), Resource 1-02C (ZY1-02C), and Gaofen-2 (GF-2) to aerial image; the accuracies are 99.09% of GF-1 WFV, 84.42% of ZY1-02C, 96.51% and 92.18% of GF-2, and close to 100% of aerial image; these results demonstrated its accuracy and robustness.

A Mini-Survey and Feasibility Study of Deep-Learning-Based Human Activity Recognition from Slight Feature Signals Obtained Using Privacy-Aware Environmental Sensors

Numerous methods and applications have been proposed in human activity recognition (HAR). This paper presents a mini-survey of recent HAR studies and our originally developed benchmark datasets of two types using environmental sensors. For the first dataset, we specifically examine human pose estimation and slight motion recognition related to activities of daily living (ADL). Our proposed method employs OpenPose. It describes feature vectors without effects of objects or scene features, but with a convolutional neural network (CNN) with the VGG-16 backbone, which recognizes behavior patterns after classifying the obtained images into learning and verification subsets. The first dataset comprises time-series panoramic images obtained using a fisheye lens monocular camera with a wide field of view. We attempted to recognize five behavior patterns: eating, reading, operating a smartphone, operating a laptop computer, and sitting. Even when using panoramic images including distortions, results demonstrate the capability of recognizing properties and characteristics of slight motions and pose-based behavioral patterns. The second dataset was obtained using five environmental sensors: a thermopile sensor, a CO2 sensor, and air pressure, humidity, and temperature sensors. Our proposed sensor system obviates the need for constraint; it also preserves each subject’s privacy. Using a long short-term memory (LSTM) network combined with CNN, which is a deep-learning model dealing with time-series features, we recognized eight behavior patterns: eating, operating a laptop computer, operating a smartphone, playing a game, reading, exiting, taking a nap, and sitting. The recognition accuracy for the second dataset was lower than for the first dataset consisting of images, but we demonstrated recognition of behavior patterns from time-series of weak sensor signals. The recognition results for the first dataset, after accuracy evaluation, can be reused for automatically annotated labels applied to the second dataset. Our proposed method actualizes semi-automatic annotation, false recognized category detection, and sensor calibration. Feasibility study results show the new possibility of HAR used for ADL based on unique sensors of two types.

Extraction of the anisotropy factor and refractive index of biological tissue in the near-infrared region from diffusion approximation in the spatial frequency domain

In order to characterize biological tissue or to model light propagation in tissue medium it is necessary to define four key optical parameters, namely, absorption coefficient μa, reduced scattering coefficient μs’, anisotropy factor g (or scattering phase function), and refractive index n. Successful derivation of these valuable parameters can provide comprehensive information regarding the condition of tissue during disease pathogenesis and therapy and help to build accurate light propagation simulator. Usually, absorption and reduced scattering coefficients are investigated while the other two are assuming to be constant factors (g≈ 0.9, n≈ 1.4) across the near-infrared region. In order to quantify the g and n spectrum of a sample, we propose the use of spatial frequency domain (SFD). In SFD, periodic illumination patterns (structured illumination) at different spatial frequencies and wavelengths are serially projected onto the sample surface. Then, the collected diffusely reflected light is analyzed via the diffusion equation (or Monte Carlo simulation) in SFD to separately recover the sample’s absorption and scattering properties over a wide field of view. This work aimed to use the solution of the diffusion equation (DE) developed for the diffuse reflectance in SFD to estimate the wavelength-dependent variability of the g and n parameters of various turbid samples in the near-infrared spectral region. Since the solution of DE contains four unknown parameters, we use four different spatial frequencies to extract these four components; four equations at four different frequencies with four unknown parameters are solved at each of the four discrete wavelengths used. During data processing of n, its wavelength-dependence was fitted using the dispersion model of Sellmeier. While there are several approaches to retrieving information about g and n, the work in SFD offers an easier and more straightforward framework and grants expanded capability to this domain by enlarging its measurement range.