Potential for spectral imaging applications on the small farm: a review

Spectral imaging technology is an emerging modality that combines the advantages of both imaging and spectroscopy (high spatial and spectral resolution) in one device. The technology has potential in numerous medical imaging and diagnostic applications. In this review, we describe the techniques used to acquire spectral images and the methods used for analyzing spectral images. We then provide detailed review about the progress of the spectral imaging technology in skin and endoscopy applications. This review also covers the recent patents on spectral imaging devices, methods, and data analysis algorithms. Keywords: Endoscopy, medical diagnosis, optical absorption, optical scattering, skin diagnosis, spectral imaging, spectroscopy, tissue reflectance.

In the present study, the advantages of multispectral imaging over hyperspectral imaging in real-time spectral imaging are briefly analyzed, and the advantages and disadvantages of snapshot spectral imaging and other spectral imaging technologies are briefly described. The technical characteristics of artificial compound eyes and multi-aperture imaging and the research significance of snapshot artificial compound eye multispectral imaging are also introduced. The classification and working principle of the snapshot artificial compound eye multispectral imaging system are briefly described. According to the realization method of the optical imaging system, the ACE snapshot multi-aperture multispectral imaging system is divided into plane and curved types. In the planar compound eye spectral imaging system, the technical progress of the multispectral imaging system based on the thin observation module by bound optics (TOMBO) architecture and the multispectral imaging system based on the linear variable spectral filter are introduced. At the same time, three curved multispectral imaging systems are introduced. Snapshot artificial compound eye multispectral imaging technology is also briefly analyzed and compared. The research results are helpful to comprehensively understand the research status of snapshot multispectral multi-aperture imaging technology based on artificial compound eyes and to lay the foundation for improving its comprehensive performance even further.

Algae and algae communities serve as indicator species in ecosystems, reflecting water body conditions and pollution levels. Accurate and efficient species identification of algae facilitates environmental monitoring and ecological research. This study proposes an algae classification method integrating spectral microscopy imaging technology with neural network algorithms. It achieves rapid identification of five algae species-Anabaenaazotica, Uronemaelongatum, Chlorella, Dunaliellasalina, and Haematococcuspluvialis-by acquiring spectral images of different algae and analyzing their absorption spectral characteristics. Separate ResNet50 convolutional neural network classification models were constructed using grayscale images and spectral images of characteristic bands. Experimental results demonstrate that the classification model based on feature spectral images achieves an accuracy of 97.14% and a recall rate of 96.67% on the test set, significantly outperforming classification based on grayscale images. This study validates the application potential of snapshot spectral microscopy in algae identification, providing a simple, rapid, and low-cost detection method for algae classification.

Spectral imaging technology continues to advance toward greater miniaturization, higher efficiency, enhanced compactness, and improved tunability. In this work, we propose a hybrid liquid-lens-based diffractive spectral array computational imaging technology powered by a parallel fused spatially calibrated 3D (PFSC3D) network. This hybrid system combines a tunable liquid zoom lens with spectral array multiplexing, offering significant advantages for spectral imaging. The liquid lens enables rapid image-plane switching by electronically adjusting its focal length, while the array detector simultaneously captures focused and defocused spectral signals across multiple wavelengths. The PFSC3D network integrates a parallel spatial calibration module into a U-Net architecture enhanced with three-dimensional convolutional layers, achieving high-precision diffractive spectral array reconstruction. The reconstructed spectral range spans 480-780 nm at 20 nm spectral resolution. The reconstructed multispectral images achieve mean peak signal-to-noise ratio (MPSNR) values of 27.77, 22.68, 23.53, and 22.44 dB for the four sub-apertures of the 2×2 array, representing an average improvement of at least 1.8 dB over the conventional Unrolled Network algorithm. This approach paves the way for the development of miniaturized and embedded spectral array sensing technologies.

Spectral imaging technology have been used mostly in remote sensing, but have recently been extended to new area requiring high fidelity color reproductions like telemedicine, e-commerce, etc. These spectral imaging systems are important because they offer improved color reproduction quality not only for a standard observer under a particular illuminantion, but for any other individual exhibiting normal color vision capability under another illuminantion. A possibility for browsing of the archives is needed. In this paper, the authors present a new spectral image browsing architecture. The architecture for browsing is expressed as follow: (1) The spectral domain of the spectral image is reduced with the PCA transform. As a result of the PCA transform the eigenvectors and the eigenimages are obtained. (2) We quantize the eigenimages with the original bit depth of spectral image (e.g. if spectral image is originally 8bit, then quantize eigenimage to 8bit), and use 32bit floating numbers for the eigenvectors. (3) The first eigenimage is lossless compressed by JPEG-LS, the other eigenimages were lossy compressed by wavelet based SPIHT algorithm. For experimental evalution, the following measures were used. We used PSNR as the measurement for spectral accuracy. And for the evaluation of color reproducibility, ΔE was used.here standard D65 was used as a light source. To test the proposed method, we used FOREST and CORAL spectral image databases contrain 12 and 10 spectral images, respectively. The images were acquired in the range of 403-696nm. The size of the images were 128*128, the number of bands was 40 and the resolution was 8 bits per sample. Our experiments show the proposed compression method is suitable for browsing, i.e., for visual purpose.

Hyper spectral imaging (HSI) has emerged as a powerful technique for accurate disease diagnosis in medical environments. It provides high-resolution images with detailed spectral information, making it possible to identify subtle differences between healthy and diseased tissues. However, current HSI systems face challenges in terms of accuracy and efficiency, limiting their widespread application in sustainable medical environments. To overcome these challenges, our team has developed an improved HSI system that utilizes state-of-the-art spectral imaging technology and machine learning algorithms. This system is capable of capturing and analyzing a wider range of spectral data, enabling more precise identification of disease-specific spectral signatures. Furthermore, our system has been optimized to operate with minimal power consumption, making it environmentally friendly and suitable for sustainable medical environments. The improved HSI system has been successfully tested in clinical settings and has shown promising results in accurately diagnosing various diseases such as cancer, dermatitis, and cardiovascular conditions. Its high accuracy and fast processing time make it a valuable tool for early disease detection and treatment planning. Moreover, the ability to operate with low energy consumption makes it a sustainable solution for medical facilities in resourcelimited areas. In addition to its accuracy and efficiency, our improved HSI system is also user-friendly and can be easily integrated into existing medical imaging systems.

Spectral imaging technologies have been used for many years by the remote sensing community. More recently, these approaches have been applied to biomedical problems, where they have shown great promise. However, biomedical spectral imaging has been complicated by the high variance of biological data and the reduced ability to construct test scenarios with fixed ground truths. Hence, it has been difficult to objectively assess and compare biomedical spectral imaging assays and technologies. Here, we present a standardized methodology that allows assessment of the performance of biomedical spectral imaging equipment, assays, and analysis algorithms. This methodology incorporates real experimental data and a theoretical sensitivity analysis, preserving the variability present in biomedical image data. We demonstrate that this approach can be applied in several ways: to compare the effectiveness of spectral analysis algorithms, to compare the response of different imaging platforms, and to assess the level of target signature required to achieve a desired performance. Results indicate that it is possible to compare even very different hardware platforms using this methodology. Future applications could include a range of optimization tasks, such as maximizing detection sensitivity or acquisition speed, providing high utility for investigators ranging from design engineers to biomedical scientists.

Spectral imaging technology has gained widespread application across diverse fields due to its ability to capture spatial and spectral information simultaneously. However, conventional spectral scanning methods using single‐peak tunable filters face the challenges of low optical throughput. Inspired by Fellgett's advantage in Fourier‐transform infrared spectroscopy, this paper proposes a tunable filter with multiple resonances to improve optical throughput. It is composed of a simple Fabry‐Pérot cavity filled with liquid crystal. An artificial neural network is employed to match with the filter for spectrum reconstruction. Experimental results show a spectral resolution of 10 nm and a switching time of ≈23 ms between adjacent states. As a demonstration, biological specimens are spectrally imaged under different light conditions with good fidelity. The results suggest that the filter possesses over six times higher optical throughput than a commercial liquid crystal tunable filter (LCTF), leading to better spectrum accuracy for spectral imaging under low‐light conditions. The compact and cost‐effective design of this tunable filter enables seamless integration into imaging systems, presenting promising prospects for practical applications such as portable health management and food inspection in low‐light conditions.

Pacific Advanced Technology (PAT) has developed an infrared hyperspectral camera based on diffractive optic arrays. This approach to hyperspectral imaging has been demonstrated in all three infrared bands SWIR, MWIR and LWIR. The hyperspectral optical system has been integrated into the cold-shield of the sensor enabling the small size and weight of this infrared hyperspectral sensor. This new and innovative approach to an infrared hyperspectral imaging spectrometer uses micro-optics that are made up of an area array of diffractive optical elements where each element is tuned to image a different spectral region on a common focal plane array. The lenslet array is embedded in the cold-shield of the sensor and actuated with a miniature piezo-electric motor. This approach enables rapid infrared spectral imaging with multiple spectral images collected and processed simultaneously each frame of the camera. This paper will present our optical mechanical design approach which results in an infrared hyper-spectral imaging system that is small enough for a payload on a small satellite, mini-UAV, commercial quadcopter or man portable. Also, an application of how this spectral imaging technology can easily be used to quantify the mass and volume flow rates of hydrocarbon gases. The diffractive optical elements used in the lenslet array are blazed gratings where each lenslet is tuned for a different spectral bandpass. The lenslets are configured in an area array placed a few millimeters above the focal plane and embedded in the cold-shield to reduce the background signal normally associated with the optics. The detector array is divided into sub-images covered by each lenslet. We have developed various systems using a different number of lenslets in the area array. Depending on the size of the focal plane and the diameter of the lenslet array will determine the number of simultaneous different spectral images collected each frame of the camera. A 2 x 2 lenslet array will image four different spectral images of the scene each frame and when coupled with a 512 x 512 focal plane array will give spatial resolution of 256 x 256 pixel each spectral image. Another system that we developed uses a 4 x 4 lenslet array on a 1024 x 1024 pixel element focal plane array which gives 16 spectral images of 256 x 256 pixel resolution each frame. This system spans the SWIR and MWIR bands with a single optical array and focal plane array.

The acquisition of spectral reflectance factor image data in an outdoor environment is a challenging task, mostly due to nonstatic scene content and illumination. In this work, we propose a work-flow for this task using a commercial Bragg-grating-based hyperspectral imager that can capture the visible and near-infrared part of the light spectrum. To our knowledge, we are the first who use this technology for outdoor spectral reflectance factor imaging. The work-flow involves focus position and exposure time estimation, illumination scaling, and image registration, among other procedures. Most of them generally apply to hyperspectral imaging, while some are specific to a Bragg-grating-based hyperspectral imaging device when dealing with specific challenges in outdoor environments. We have conducted some experiments to evaluate the quality of the acquired image data and discussed some limitations of the technology for spectral imaging of outdoor scenes. Fourteen urban scene spectral images acquired using the proposed approach are already publicly available to the scientific community under a Creative Commons license.

The spectral polarization imager can detect the spectral polarization information of the target reflection or radiated light that cannot be obtained by ordinary optical instruments. The obtained spectral polarization image can provide richer target information than the intensity image and the spectral image. At the same time, being able to achieve snapshot imaging and improve the spectral resolution is the research and development direction of polarization spectrum imaging technology. In this paper, we present a dual channel snapshot compressive spectral polarization imaging technique for simultaneous acquisition of two-dimensional intensity information, one-dimensional spectral information, and four-dimensional polarization information of a target in visible range. One channel is based on a coded mask and micro-polarizer array, and one channel is based on a pixel-level polarizer array detector. The main optical path replaces the ordinary detector with a micro-polarizer array based on CASSI. The micro-polarizer array consists of 0°, 45°, 90°, and 135° linear micro-polarizers regularly distributed, and each pixel matches the pixel of the detector. The three Stokes parameters of the scene are compressed and sensed, and a four-dimensional (4D) data cube is projected onto a two-dimensional (2D) focal plane. Through nonlinear optimization with sparsity constraints, a 4D spectral polarization data cube is reconstructed from 2D measurements. The addition of a pixel-level polarizer array detector helps to improve the measurement accuracy of spectral information and polarization information. Optical experimental results confirm that the architecture reduces the total number of measurements required to obtain a spectrally polarized image compared to traditional acquisition methods. The dual channel combination enables simultaneous acquisition of spectral and polarization information, and improves the quality of reconstructed image based on compressed sensing algorithm. A dual-channel experimental device with coded aperture spectral polarization imaging channel and polarization imaging channel was set up to obtain spectral data cubes with 4 polarization states in 25 bands in the range of 450nm-650nm, and the polarization degree and polarization angle of each band. The spectral resolution was better than 10nm, and the spectral restoration accuracy was about 86.3%. Compared with the single-channel imaging method, the spectral reconstruction accuracy was improved by 10.5%.This has guiding significance for the design and research of light and miniaturized hyperspectral polarization imagers in the future. It is expected to be widely used in astronomical observation, atmospheric detection, biomedical diagnosis, earth environment monitoring, target detection and identification and other fields.

Hyperspectral and multispectral imaging for evaluating food safety and quality

Study on microscope hyperspectral medical imaging method for biomedical quantitative analysis

Hyperspectral imaging (HSI) systems have been demonstrated as a powerful imaging technique due to their high spectral resolution. HSI can obtain the spectrum for each pixel in the image of a scene, a feature that can be exploited to design optical systems with the purpose of analyzing and characterizing objects and identifying processes within the visible electromagnetic spectrum (bandwidth). In this paper, we present an HSI system comprising a diffraction grating placed in the exit pupil of our optical configuration. The spectrum for each pixel associated with the object appears in the first order of diffraction. We used this system to characterize and tune the required spectral band of the image of the captured object obtaining more information than with an optical imaging system. Accordingly, the proposed optical system is suitable to obtain spectral and hyperspectral imaging at low cost compared to an acousto-optic system or other HSI. The scanning system captures hundreds of spectral images associated with the object, obtaining a maximum spectral resolution of 0.26nm or 260 pm for one of our configurations.

Background: The high-precision analysis of multimode fibers (MMFs) is a critical task in numerous applications, including remote sensing, medical imaging, and environmental monitoring. In this study, we propose a novel deep interpretable network approach to reconstruct spectral images captured using CCD sensors. Methods: Our model leverages a Tiny-YOLO-inspired convolutional neural network architecture, specifically designed for spectral wavelength prediction tasks. A total of 1880 CCD interference images were acquired across a broad near-infrared range from 1527.7 to 1565.3 nm. To ensure precise predictions, we introduce a dynamic factor α and design a dynamic adaptive loss function based on Huber loss and Log-Cosh loss. Results: Experimental evaluation with five-fold cross-validation demonstrates the robustness of the proposed method, achieving an average validation MSE of 0.0149, an R2 score of 0.9994, and a normalized error (μ) of 0.0005 in single MMF wavelength prediction, confirming its strong generalization capability across unseen data. The reconstructed outputs are further visualized as smooth spectral curves, providing interpretable insights into the model’s decision-making process. Conclusions: This study highlights the potential of deep learning-based interpretable networks in reconstructing high-fidelity spectral images from CCD sensors, paving the way for advancements in spectral imaging technology.