Accuracy Of Spectral Reconstruction Research Articles

Error analysis based on a tunable wave plate polarization interferometric imaging spectrometer

Interference imaging spectroscopy combines modern imaging technology with spectral technology, holding significant importance for object imaging and spectral detection. This article introduces the principle of an adjustable wave plate polarization interferometric imaging spectrometer. The example design specifications are set for an observation wavelength range of 450–780 nm and a maximum resolution of 2 nm at 450 nm, with a 0.5 in detector as the base for calculating the specific dimensions of the Soleil–Babinet compensator. An investigation was conducted on the issues of nonuniform sampling, as well as three types of mechanical errors: flatness, wedge angle tolerance, and optical axis orientation accuracy. Emphasis was placed on discussing the impact of these errors on the instrument’s optical path difference and spectral reconstruction accuracy. This research provides theoretical guidance for the design and engineering of this miniaturized imaging spectrometer.

Snapshot computational spectroscopy enabled by deep learning

Abstract Spectroscopy is a technique that analyzes the interaction between matter and light as a function of wavelength. It is the most convenient method for obtaining qualitative and quantitative information about an unknown sample with reasonable accuracy. However, traditional spectroscopy is reliant on bulky and expensive spectrometers, while emerging applications of portable, low-cost and lightweight sensing and imaging necessitate the development of miniaturized spectrometers. In this study, we have developed a computational spectroscopy method that can provide single-shot operation, sub-nanometer spectral resolution, and direct materials characterization. This method is enabled by a metasurface integrated computational spectrometer and deep learning algorithms. The identification of critical parameters of optical cavities and chemical solutions is demonstrated through the application of the method, with an average spectral reconstruction accuracy of 0.4 nm and an actual measurement error of 0.32 nm. The mean square errors for the characterization of cavity length and solution concentration are 0.53 % and 1.21 %, respectively. Consequently, computational spectroscopy can achieve the same level of spectral accuracy as traditional spectroscopy while providing convenient, rapid material characterization in a variety of scenarios.

Miniature snapshot mid-infrared spectrometer based on metal-insulator-metal metasurface

Metasurfaces showcase the performance of light field manipulation at the subwavelength scale, generating tremendous applications in the field of optical imaging and sensing, especially in spectroscopic detection. Here, we demonstrate a spectral detector comprising metal-insulator-metal composite structures working in the mid-infrared band, which can effectively collect and restore target spectral characteristics in the mid-infrared band with a trained reconstruction algorithm. The proposed device consists of snapshot multichannel detection and spectral reconstruction, showing an average spectral reconstruction accuracy approaching 80% of the system. Moreover, we discuss the feasibility of applying this structural design to a miniature spectrometer over a wider infrared wavelength range by proposing a feasible design strategy. Our results provide a novel approach for low-cost and portable mid-infrared spectroscopic detection in ultracompact mid-infrared spectral imaging and sensing elements.

CTIS spectral image reconstruction technology based on slit-scanning architecture.

The computed tomography imaging spectrometer (CTIS) is a snapshot imaging spectrometer, excelling in dynamic detection tasks. It can capture two-dimensional spatial information and spectrally compressed information of a target within a single exposure time. However, traditional CTIS image reconstruction algorithms suffer from missing-cone problem, which reduces the accuracy of spectral reconstruction. In recent years, deep learning has been applied to CTIS spectral image reconstruction, significantly improving spectral reconstruction accuracy compared to traditional algorithms. However, due to the missing-cone problem, it is difficult to accurately recover the truth of spectral data cube in the real scene. Currently, most CTIS neural network reconstruction models are trained using simulated datasets of spectral data cubes and diffractive images. Because these data differ significantly from real data under actual application conditions, the established models may not be effectively applicable to real-world scenes. Therefore, we propose a new CTIS system based on slit-scanning architecture utilizing an adjustable slit aperture to obtain the real spectral data cube of the target while maintaining the simplicity of the CTIS structure. By limiting the field of view (FOV) through the slit, the area of diffraction overlap can be reduced, thereby enhancing the accuracy of CTIS spectral reconstruction using the expectation-maximization (EM) algorithm. This architecture allows us to obtain accurate spectral cubes that match the CTIS diffractive image of real-world scenes, providing a real dataset for training the reconstruction network. A prototype has been built to demonstrate the feasibility of our proposed solution. Furthermore, we also constructed a residual network based on multi-scale and attention mechanism. This network is trained using a combination of simulated and real spectral imaging data. Compared to the reconstruction performance of the EM algorithm and convolutional neural networks, our approach demonstrates superior spectral reconstruction accuracy, validating the importance of real spectral data in CTIS spectral reconstruction tasks.

Research on system spectral response functions’ calibration and data processing methods for computational imaging spectrometers based on filter modulation

To enhance the performance of the optical payload, a novel computational imaging spectrometer using a board-bandpass film-filter line array (IS-BFLA) was developed. Since there is an obvious difference between the IS-BFLA and the one utilizing dispersion, conventional calibration and evaluation methods are not suitable. In this paper, we present a calibration method for the IS-BFLA. The calibration objects are determined by the mathematical model. We develop an automated calibration system and design a set of data processing procedures. Two evaluation methods: self-evaluation of multi-measurement based on Euclidean distance and multi-pixel inter-evaluation based on spectral angle, are developed to evaluate the precision and accuracy of the calibration. Furthermore, comparing the calibration value with the design value and spectral reconstruction are also utilized. The accuracy of spectral reconstruction: R2 is greater than 0.99, which determines the feasibility of the calibrated data. We hope that the work in this paper will assist in subsequent related studies.

Spectral reconstruction of fundus images using retinex-based semantic spectral separation transformer, applied for retinal oximetry

Multispectral imaging, a non-invasive technique to measure retinal oxygen saturation levels, is limited due to its time-consuming and expensive nature. In this paper, we present R3ST (Retinex-based Semantic Spectral Separation Transformer), an innovative end-to-end method for RGB fundus image spectral reconstruction. Our proposed model leverages RGB images to reconstruct multispectral images, making retinal oximetry more accessible for diagnostics.Existing spectral reconstruction methods face challenges in achieving high accuracy and generalizing across camera sources, affecting diagnostic results. R3ST addresses these issues through an unsupervised semantic spectral separation module that strives to encourage the separate reconstruction of pixels with different semantics as much as possible. Additionally, we introduce an oxygen saturation loss to improve reconstruction accuracy within the relevant regions. To overcome the limitations of generalization properties, our model incorporates a self-supervised Retinex reflectance extraction module. This simulates various imaging systems and extracts reflectance information from fundus images for reconstruction.Extensive experiments demonstrate R3ST’s superior performance in spectral reconstruction accuracy, making retinal oximetry a more viable, cost-effective diagnostic option that provides essential eye health and systemic health information. The code of R3ST is available at https://github.com/telesc0pe/R3ST.

Low-rank reconstruction for mid-infrared spectroscopy using broadband ultra-wide-angle photonic filters and IReg algorithm

In this study, a broadband mid-infrared photonic filter is designed. It has the advantages of polarization insensitivity, ultrawide angle, high manufacturability, and easy integration. For infrared spectrum reconstruction, the use of filters as spectral sampling channels can eliminate the influence of slits or complex optical splitting elements in spectral imaging technology. Further, we propose a low-rank matrix logarithm regularization algorithm based on a three-segment filter function (IReg algorithm) combined with the designed filter for a variety of mid-infrared detector front ends, which can significantly suppress noise interference. Here, we demonstrate that the stability of the spectral reconstruction accuracy under the large disturbance environment obtained by the IReg algorithm is much higher than those of the L2 and L1 algorithms. In the low-dimensional matrix state, the accuracy and stability of the ill-conditioned linear equations are guaranteed, the anti-noise ability is enhanced, the effect of data dimensionality reduction is realized, and the accuracy of mid-infrared spectral reconstruction is improved.

Tensor-Based Sparse Representation for Hyperspectral Image Reconstruction Using RGB Inputs

Hyperspectral image (HSI) reconstruction from RGB input has drawn much attention recently and plays a crucial role in further vision tasks. However, current sparse coding algorithms often take each single pixel as the basic processing unit during the reconstruction process, which ignores the strong similarity and relation between adjacent pixels within an image or scene, leading to an inadequate learning of spectral and spatial features in the target hyperspectral domain. In this paper, a novel tensor-based sparse coding method is proposed to integrate both spectral and spatial information represented in tensor forms, which is capable of taking all the neighboring pixels into account during the spectral super-resolution (SSR) process without breaking the semantic structures, thus improving the accuracy of the final results. Specifically, the proposed method recovers the unknown HSI signals using sparse coding on the learned dictionary pairs. Firstly, the spatial information of pixels is used to constrain the sparse reconstruction process, which effectively improves the spectral reconstruction accuracy of pixels. In addition, the traditional two-dimensional dictionary learning is further extended to the tensor domain, by which the structure of inputs can be processed in a more flexible way, thus enhancing the spatial contextual relations. To this end, a rudimentary HSI estimation acquired in the sparse reconstruction stage is further enhanced by introducing the regression method, aiming to eliminate the spectral distortion to some extent. Abundant experiments are conducted on two public datasets, indicating the considerable availability of the proposed framework.

Design of a Dual-Mode Multispectral Filter Array.

Multispectral imaging is valuable in many vision-related fields as it provides an additional modality to observe the world. Cameras equipped with multispectral filter arrays (MSFAs) are typically impractical for everyday use due to their intractable demosaicking and chromatic reproduction processes, which restrict their applicability beyond academic research. In this work, a novel MSFA design is proposed to enable dual-mode imaging for multispectral cameras. In addition to a conventional multispectral image, the camera is also able to produce a Bayer-formed RGB image from a single shot by grouping and merging adjacent pixels in the proposed MSFA, making it suitable for scenarios where display-ready RGB images are required. Furthermore, a two-stage optimization scheme is implemented to jointly optimize objective functions for both imaging modes. The evaluation results on multiple datasets suggest that the proposed MSFA design is able to simultaneously achieve competitive spectral reconstruction accuracy compared to elaborate multispectral cameras and chromatic accuracy compared to commercial RGB cameras.

Reconstruction of spectral irradiance in a real application with a multi-channel spectral sensor using convolutional neural networks.

Lighting is not only a key mediator for the perception of the architectural space but also plays a crucial role regarding the long-term well-being of its human occupants. Future lighting solutions must therefore be capable of monitoring lighting parameters to allow for a dynamic compensation of temporal changes from the optimal or intended conditions. Although mostly based on synthetic data, previous studies adopting small, low-cost, multi-band color sensors for this kind of parameter estimation have reported some promising preliminary results. Building up on these findings, the present work introduces a new methodology for estimating the absolute spectral irradiances of real-world lighting scenarios from the responses of a 10-channel spectral sensor by using a convolutional neural network approach. The lighting scenarios considered here are based on a tunable white floor lamp system set up at three different indoor locations and comprise combinations of LED, fluorescent, tungsten, and daylight lighting conditions. For white light mixtures of the various spectral components, the proposed reconstruction methodology yields estimates of the spectral power distribution with an average root-mean-square error of 1.6%, an average Δu'v' of less than 0.001, and an average illuminance accuracy of 2.7%. Sensor metamerism is discussed as a limiting factor for the achievable spectral reconstruction accuracy with certain light mixtures.

Lightweight computational spectrometer enabled by learned high-correlation optical filters.

A neural network (NN) computational spectrometer has high reconstruction accuracy and a fast operation speed; however, this type of spectrometer also occupies a large amount of storage in an embedded system due to the excessive computation volume. Contrarily, conventional algorithms such as gradient projection for sparse reconstruction (GPSR) take up less storage, but their spectral reconstruction accuracy is much lower than that of an NN. The major reason is that the performance of a GPSR depends greatly on the non-correlation property of optical filters which may pose challenges for optical filters design and fabrication. In this study, a GPSR algorithm, known as NN-GPSR, is applied to achieve high-precision spectral reconstruction enabled by NN-learned highly correlated filters. A group of NN-learned filters shows high-correlation work as the encoder, and an optimized GPSR algorithm works as the decoder. In this case, large computation volume is exempt and prior knowledge of tens of thousands of images are exploited to get appropriate optical filters design. The experiment data indicate that the NN-GPSR performs well in the reconstructing spectrum and requires far less storage.

Research on the Spectral Reconstruction of a Low-Dimensional Filter Array Micro-Spectrometer Based on a Truncated Singular Value Decomposition-Convex Optimization Algorithm

Currently, the engineering of miniature spectrometers mainly faces three problems: the mismatch between the number of filters at the front end of the detector and the spectral reconstruction accuracy; the lack of a stable spectral reconstruction algorithm; and the lack of a spectral reconstruction evaluation method suitable for engineering. Therefore, based on 20 sets of filters, this paper classifies and optimizes the filter array by the K-means algorithm and particle swarm algorithm, and obtains the optimal filter combination under different matrix dimensions. Then, the truncated singular value decomposition-convex optimization algorithm is used for high-precision spectral reconstruction.In terms of spectral evaluation, due to the strong randomness of the target detected during the working process of the spectrometer, the standard value of the target spectrum cannot be obtained. Therefore, we adopt the method of joint cross-validation of multiple sets of data for spectral evaluation. The results show that when the random error of +/− 2 code values is applied multiple times for reconstruction, the spectral angle cosine value between the reconstructed curves becomes more than 0.995, which proves that the spectral reconstruction under this algorithm has high stability. At the same time, the spectral angle cosine value of the spectral reconstruction curve and the standard curve can reach above 0.99, meaning that it realizes a high-precision spectral reconstruction effect. A high-precision spectral reconstruction algorithm based on truncated singular value-convex optimization, is established in this paper, providing important scientific research value for the engineering application of micro-spectrometers.

Sensor simulation using a spectrum tunable LED system.

This study developed a method to simulate the sensor responses and verify the effectiveness on spectral reconstruction by a spectrum tunable LED system. Studies have shown that the spectral reconstruction accuracy could be improved by including multiple channels in a digital camera. However, the real sensors with designed spectral sensitivities were hard to manufacture and validate. Therefore, the presence of a quick and reliable validation mechanism was preferred when performing evaluation. In this study, two novel approaches, i.e., channel-first and illumination-first simulations, were proposed to replicate the designed sensors with the use of a monochrome camera and a spectrum-tunable LED illumination system. In the channel-first method, the spectral sensitivities of three extra sensor channels were optimized theoretically for an RGB camera and then simulated by matching the corresponding illuminants in the LED system. The illumination-first method optimized the spectral power distribution (SPD) of the lights using the LED system, and the extra channels could be determined accordingly. The results of practical experiments showed that the proposed methods were effective to simulate the responses of the extra sensor channels.

Superposition Fabry-Perot filter array for a computational hyperspectral camera.

Computational hyperspectral cameras with broadband encoded filter arrays enable high precision spectrum reconstruction with only a few filters. However, these types of hyperspectral cameras have limited application, because it is difficult for conventional encoded filter arrays to balance among the spectrum regulation capacity, angle insensitivity, and processibility. This Letter presents a new, to the best of our knowledge, encoded filter composed of superposition Fabry-Perot resonance cavity (SFP) that can simultaneously take all three aspects into consideration. By learning the parameters of an SFP encoder and a neural network decoder in an end-to-end manner, a computational hyperspectral camera based on an SFP filter array presents up to 2.24 times higher spectral reconstruction accuracy, 10 times wider working angle, and can be produced with a low-cost manufacturing process.

Analysis Model of Image Colour Data Elements Based on Deep Neural Network.

At present, the classification method used in image colour element analysis in China is still based on subjective visual evaluation. Because the evaluation process will inevitably be disturbed by human factors, it will not only have low efficiency but also produce large errors. To solve the above problems, this paper proposes an image colour data element analysis model based on depth neural network. Firstly, intelligent analysis of image colour data elements based on tensorflow is constructed, and the isomerized tensorflow framework is designed with the idea of Docker cluster to improve the efficiency of image element analysis. Secondly, considering the time error and spatial error diffusion model in the process of image analysis, the quantization modified error diffusion model is replaced by the original model for more accurate colour management. Image colour management is an important link in the process of image reproduction; the rotating principal component analysis method is used to correct and analyze the image colour error. Finally, using the properties of transfer learning and convolution neural network, an image colour element analysis model based on depth neural network is established. Large-scale image data is collected, and the effectiveness and reliability of the algorithm are verified from different angles. The results show that the new image colour analysis method can not only reveal the true colour components of the target image; furthermore, the real colour component of the target image also has high spectral data reconstruction accuracy, and the analysis results have strong adaptability.

Convolutional neural network-based spectrum reconstruction solver for channeled spectropolarimeter.

Channeled spectropolarimetry is a snapshot technique for measuring the spectra of Stokes parameters of light by demodulating the measured spectrum. As an indispensable part of the channeled spectropolarimeter, the spectrometer module is far from being perfect to reflect the real modulation spectrum, which further reduces the polarimetric reconstruction accuracy of the channeled spectropolarimeter. Since the modulation spectrum is composed of many continuous narrow-band spectra with high frequency, it is a challenging work to reconstruct it effectively by existing methods. To alleviate this issue, a convolutional neural network (CNN)-based spectral reconstruction solver is proposed for channeled spectropolarimeter. The key idea of the proposed method is to first preprocess the measured spectra using existing traditional methods, so that the preprocessed spectra contain more spectral features of the real spectra, and then these spectral features are employed to train a CNN to learn a map from the preprocessed spectra to the real spectra, so as to further improve the reconstruction quality of the preprocessed spectra. A series of simulation experiments and real experiments were carried out to verify the effect of the proposed method. In simulation experiments, we investigated the spectral reconstruction accuracy and robustness of the proposed method on three synthetic datasets and evaluate the effect of the proposed method on the demodulation results obtained by the Fourier reconstruction method. In real experiments, system matrices are constructed by using measured spectra and reconstructed spectra respectively, and the spectra of Stokes parameters of incident light are estimated by the linear operator method. Several other advanced demodulation methods are also used to demodulate the measured spectrum in both simulation and real experiments. The results show that compared with other methods, the accuracy of the demodulation results can be much more improved by employing the CNN-based solver to reconstruct the measured spectrum.

Characterization of the Autoencoder Radiation Anomaly Detection (ARAD) model

In this work we demonstrate an in-depth analysis and characterization of the Autoencoder Radiation Anomaly Detection (ARAD) algorithm. ARAD is a deep convolutional autoencoder designed to detect anomalous radioactive signatures in gamma-ray spectra collected by NaI(Tl) detectors. This model works by learning a dimensionally constrained representation of background gamma-ray spectra called the latent space. The latent space cannot fully describe anomalous components in new spectra, resulting in a decrease in spectral reconstruction accuracy that triggers an alarm. This paper demonstrates the model’s performance on a set of data collected outside of the High Flux Isotope Reactor and Radiochemical Engineering Development Center facilities at Oak Ridge National Laboratory. We also perform an evaluation of the model’s detection performance using a set of publicly available synthetic data representing a radiation detector moving throughout an urban city street. We demonstrate the algorithm’s ability to detect sources in locations with highly dynamic background count rates resulting from variations in naturally occurring radioactive materials and precipitation-induced radon washout, a challenge for many traditional radiation detection algorithms. We compare these results against those from another unsupervised radiation anomaly detection algorithm based on principal component analysis. ARAD achieved excellent performance on both datasets and proves the viability and efficacy of autoencoders for radiation anomaly detection.

Physics-Informed Hyperspectral Remote Sensing Image Synthesis With Deep Conditional Generative Adversarial Networks

High-resolution hyperspectral remote sensing images are of great significance to agricultural, urban, and military applications. However, collecting and labeling hyperspectral images are time-consuming, expensive, and usually heavily rely on domain knowledge. In this article, we propose a new method for generating high-resolution hyperspectral images and subpixel ground-truth annotations from RGB images. Given a single high-resolution RGB image as its conditional input, unlike previous methods that directly predict spectral reflectance and ignores the physics behind it, we consider both imaging mechanism and spectral mixing, introduce a deep generative network that first recovers the spectral abundance for each pixel, and then generate the final spectral data cube with the standard USGS spectral library. In this way, our method not only synthesizes high-quality spectral data existing in the real world but also generates subpixel-level spectral abundance with well-defined spectral reflectance characteristics. We also introduce a spatial discriminative network and a spectral discriminative network to improve the fidelity of the synthetic output from both spatial and spectral perspectives. The whole framework can be trained end-to-end in an adversarial training paradigm. We refer to our method as “Physics-informed Deep Adversarial Spectral Synthesis (PDASS).” On the IEEE <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grss_dfc_2018</i> dataset, our method achieves an MPSNR of 47.56 on spectral reconstruction accuracy and outperforms other state-of-the-art methods. As latent variables, the generated spectral abundance and the atmospheric absorption coefficients of sunlight also suggest the effectiveness of our method.

Designing an Ultrathin Film Spectrometer Based on III-Nitride Light-Absorbing Nanostructures.

In this paper, a spectrometer design enabling an ultrathin form factor is proposed. Local strain engineering in group III-nitride semiconductor nanostructured light-absorbing elements enables the integration of a large number of photodetectors on the chip exhibiting different absorption cut-off wavelengths. The introduction of a simple cone-shaped back-reflector at the bottom side of the substrate enables a high light-harvesting efficiency design, which also improves the accuracy of spectral reconstruction. The cone-shaped back-reflector can be readily fabricated using mature patterned sapphire substrate processes. Our design was validated via numerical simulations with experimentally measured photodetector responsivities as the input. A light-harvesting efficiency as high as 60% was achieved with five InGaN/GaN multiple quantum wells for the visible wavelengths.

Input aperture restriction of the spatial spectral compressive spectral imager and a comprehensive solution for it.

Compressive spectral imaging (CSI) is an attractive spectral imaging technique since it could acquire a spectral image data cube in a single snapshot. One notable CSI scheme is the spatial spectral compressive spectral imager (SSCSI), which has low complexity and high quality of the recovering spectral image. However, the SSCSI suffers from a small input aperture, which reduces the optical efficiency and signal-to-noise ratio of the system. In this paper, the effect of the input aperture size on the SSCSI system is analyzed. It shows that with the increase of input aperture, the incident light from different spectral bands will overlap with each other on the mask, and the encoding pattern of each spectral band will be ambiguous. Thus, the reconstruction quality of the data cube will highly deteriorate. A new scheme is proposed to deal with this problem. First, the observed image is resampled and recombined into new sub-observed images to improve the frequency response of the encoding pattern. Then each sub-observed image is divided into multiple sub-sets to reduce the coherence of the sensing matrix. Compared to the original reconstruction algorithm for the SSCSI system, the peak signal-to-noise ratio (PSNR) is promoted by more than 3dB, and the spectral reconstruction accuracy and noise suppression capability are also improved.