Light Imaging Research Articles

Image enhancement method for paint defects based on polarization fusion technique

The highly reflective nature of paint surfaces poses a challenge for defect detection. In this paper, a polarization fusion based image enhancement algorithm for paint defects is proposed to improve the image quality and complement the masked defect details. Firstly, the linearly polarized photometric (DOLP) image is initially fused with the angle of polarization (AOP) image. The light intensity images and polarization feature images were enhanced using a single scale Retinex algorithm and these images were decomposed and reconstructed using wavelet transform to integrate the low and high frequency subbands based on saliency maps and regional energy features. Experiments were performed on defects at different locations and compared with other classical methods. The results show that the proposed fusion algorithm improves the contrast while providing more complete defect information, clearer texture details and significant advantages over other algorithms.

SIFusion: Lightweight infrared and visible image fusion based on semantic injection.

The objective of image fusion is to integrate complementary features from source images to better cater to the needs of human and machine vision. However, existing image fusion algorithms predominantly focus on enhancing the visual appeal of the fused image for human perception, often neglecting their impact on subsequent high-level visual tasks, particularly the processing of semantic information. Moreover, these fusion methods that incorporate downstream tasks tend to be overly complex and computationally intensive, which is not conducive to practical applications. To address these issues, a lightweight infrared and visible light image fusion method known as SIFusion, which is based on semantic injection, is proposed in this paper. This method employs a semantic-aware branch to extract semantic feature information, and then integrates these features into the fused features through a Semantic Injection Module (SIM) to meet the semantic requirements of high-level visual tasks. Furthermore, to simplify the complexity of the fusion network, this method introduces an Edge Convolution Module (ECB) based on structural reparameterization technology to enhance the representational capacity of the encoder and decoder. Extensive experimental comparisons demonstrate that the proposed method performs excellently in terms of visual appeal and advanced semantics, providing satisfactory fusion results for subsequent high-level visual tasks even in challenging scenarios.

Effect of detachment on Magnum-PSI ELM-like pulses: direct observations and qualitative results

Abstract Conditions similar to those at the end of the divertor leg in a tokamak were replicated in the linear plasma machine Magnum-PSI. The neutral pressure in the target chamber is then increased to cause the target to transition from an attached to a detached state. Superimposed to this steady state regime, edge localised mode (ELM)-like pulses are reproduced, resulting in a sudden increase in plasma temperature and density, such that the heat flux increases transiently by half an order of magnitude. Visible light emission, target thermography, and Thomson scattering are used to demonstrate that the higher the neutral pressure the more energy is removed from the ELM-like pulse in the volume. If the neutral pressure is sufficiently high, the ELM-like pulse can be prevented from affecting the target and the plasma energy is fully dissipated in the volume instead (ID 4 in table 1). The visible light images allow the division of the ELM-plasma interaction process of ELM energy dissipation into 3 ‘stages’ ranging from no dissipation to full dissipation (the target plasma is detached). In the second publication related to this study, spectroscopic data is analysed with a Bayesian approach, to acquire insights into the significance of molecular processes in dissipating the plasma energy and particles.

Fast and light-efficient remote focusing for volumetric voltage imaging

Voltage imaging holds great potential for biomedical research by enabling noninvasive recording of the electrical activity of excitable cells such as neurons or cardiomyocytes. Camera-based detection can record from hundreds of cells in parallel, but imaging entire volumes is limited by the need to focus through the sample at high speeds. Remote focusing techniques can remedy this drawback, but have so far been either too slow or light-inefficient. Here, we introduce flipped image remote focusing, a remote focusing method that doubles the light efficiency compared to conventional beamsplitter-based techniques and enables high-speed volumetric voltage imaging at 500 volumes/s. We show the potential of our approach by combining it with light sheet imaging in the zebrafish spinal cord to record from >100 spontaneously active neurons in parallel.

LEAFusion: An Infrared and Visible Light Image Fusion Network Resilient to Harsh Light Environment Interference Based on Harsh Light Environment Aware

LEAFusion: An Infrared and Visible Light Image Fusion Network Resilient to Harsh Light Environment Interference Based on Harsh Light Environment Aware

Analysis of clinicopathological factors associate with the visibility of early gastric cancer in endoscopic examination and usefulness of linked color imaging: A multicenter prospective study.

This study investigated clinicopathological factors associated with the visibility of early gastric cancer and the efficacy of linked color imaging. Patients with early gastric cancer who underwent endoscopic treatment between April 2021 and July 2022 were enrolled. All cases underwent white light imaging and linked color imaging. Three experts evaluated lesion visibility using a visual analog scale. A mean score ≥3 on white light imaging was defined as "good visibility", and <3 as "poor visibility". We extracted patient information and endoscopic and pathological data for the lesion and background mucosa, analyzed factors associated with the visibility of early gastric cancer, and compared visibility between white light imaging and linked color imaging. Ninety-seven lesions were analyzed, with good visibility in 49 and poor visibility in 48. Multivariate analysis revealed small lesion size (odds ratio 1.89) and presence of endoscopic intestinal metaplasia (odds ratio 0.49) as significantly associated with the poor visibility of early gastric cancer. Mean visibility score was significantly higher for linked color imaging (P<0.001). Mean score for linked color imaging was significantly higher in the poor visibility group (P<0.001), but not significantly different in the good visibility group (P = 0.292). Mean score was significantly higher with linked color imaging in cases with endoscopic intestinal metaplasia (P = 0.0496) and lesions <20 mm in diameter (<10 mm, P = 0.002; 10-20 mm, P = 0.004). Lesion size and endoscopic intestinal metaplasia are associated with the visibility of early gastric cancer in white light imaging. Linked color imaging improves visibility of gastric cancer with these factors.

Blue light reflectance imaging in non-perfusion areas detection: insights from multimodal analysis

DesignA retrospective, cross-sectional image analysis using a convenience sample.SubjectsFive cases selected based on the availability of comprehensive imaging data.MethodsThis study involved a retrospective review of images from five cases, focusing on the use of retinal monochromatic blue light reflectance (BLR) imaging to detect non-perfusion areas. Two cases of sickle-cell retinopathy demonstrated peripheral retinal non-perfusion identified through widefield fluorescein angiography. Three other cases—one with branch retinal vein occlusion, one with branch retinal artery occlusion, and one presenting paracentral acute middle maculopathy showed focal macular non-perfusion detected by structural OCT and OCTA. The areas of nonperfused retinal tissue, confirmed by fluorescein angiography, OCT, and OCTA, were then correlated with findings from the BLR image. This correlation aimed to identify any potential associations between these imaging modalities.Main outcome measuresEnhance understanding of the utilization of retinal monochromatic BLR images as a non-perfusion biomarker.ResultsThe perfusion defects identified through fluorescein angiography were qualitatively correlated with hypo-reflective regions observed in the BLR images. A notable correlation was also observed between the OCTA deep capillary plexus findings and the BLR images. Additionally, areas of retinal thinning identified on structural OCT thickness maps corresponded with the hypo-reflective regions in the BLR images. This indicates the potential of BLR in identifying non-perfused retinal areas.ConclusionsThis study reinforces the evidence, through OCT, OCTA, and angiographic correlation, that the BLR can effectively identify areas of retinal non-perfusion in a non-invasive manner. Further research is warranted to assess the method’s sensitivity, specificity, and limitations. While the interaction of blue light with the retina, leading to specular reflections and scattering, is established, this research represents a pioneering effort in suggesting which specific retinal structures may be implicated in this phenomenon. This novel insight opens avenues for deeper exploration into the underlying mechanisms and potential clinical applications of utilizing the BLR imaging technique for assessing retinal vascular abnormalities.

Increases in Night Lights Intensity Reveal Extreme Events: A Case of Study on the Ongoing Conflict in Ukraine

Abstract. Analyzing night lights images allows identifying and monitoring affected areas during conflicts, as an overall decrease in brightness indicates abandoned or destroyed buildings, curfews, or power shortages. While detecting decrease in lights intensity is an established technique to identify critical areas during conflicts, the causes of appearing bright lights have not been extensively studied. An increase in brightness may be related to extreme events, such as fires resulting from bombing. This paper shows several examples supporting this new use of these images, validated using high-resolution optical earth observation data and ancillary information sources in the frame of the conflict in Ukraine which has been ongoing since February 2022.

Edge enhancement and feature modulation based network for light field depth estimation

Estimating depth from light field images is a critical issue in light field applications. While learning-based methods have made significant strides in light field depth estimation, achieving high accuracy and speed simultaneously remains a major challenge. This paper proposes a light field depth estimation network based on edge enhancement and feature modulation, which significantly improves depth estimation results by emphasizing inter-view correlations while preserving image edge features. Specifically, to prioritize edge details, we introduce an Edge-Enhanced Cost Constructor (EECC) that integrates edge information with existing cost constructors to improve depth estimation performance in complex areas. Furthermore, most light field depth estimation networks utilize only sub-aperture images (SAIs) without considering the inherent angular information in macro-pixel image (MacPI). To address this limitation, we propose the MacPI-Guided Feature Modulation (MGFM) module, which leverages angular information between different views in MacPI to modulate features at each view. Experimental results show that our method not only performs excellently on synthetic datasets but also demonstrates outstanding generalization on real-world datasets, achieving a better balance between accuracy and computation speed.

Illuminating Causality: The Role of Light in Thomas Aquinas’s Metaphysics of Being

In this article, we hold a strong thesis, i.e., that light is the conceptual resource that best explains the creative act in Thomas Aquinas’s metaphysics. Thus, inasmuch as being is the luminous act of the Uncreated Being, it bears resemblance to the Uncreated Being in its very principle. In this sense, we seek to present the role of light, in its meaning as manifestation, as an act that belongs to being. Indeed, in contrast with some contemporary interpretations, which place light within the order of formal causality, we argue that light, in its belonging to Aquinas’s participatory metaphysics, pertains to the order of efficient causality. Therefore, we hold that light has strong explanatory pretensions in Aquinas’ thought. Accordingly, after an introduction (1), this article is divided into the following sections: (2) The Meaning of Divine Causality; (3) Being as Light; (4) The Manifestative Character of Light; (5) Light Images: Analogies and Metaphors; and (6) Conclusions.

Weak saliency ensemble network for person Re-identification using infrared light images

In recent years, person re-identification (re-id) has primarily been studied using visible light (VL) images. However, the challenges of employing VL images in nighttime environments have prompted research into using infrared light (IR) images. Yet, the utilization of both VL and IR images in person re-id has resulted in increased computational cost and processing time in multi-modality systems, leading to studies focusing solely on IR images. Nevertheless, IR images, lacking color and texture information, generally yield lower recognition performance in existing person re-id studies. In addition, previous studies have shown that person re-id performance suffers in the presence of complex background noise. To tackle these challenges, this study proposes a new weak saliency ensemble network (WSE-Net) for person re-id using IR images. WSE-Net incorporates a channel reduction of feature (CRF) method to reduce computational cost in the ensemble network, a technique for converting input images into group of patch images and feeding them into the ensemble model to enhance the reduced feature information, and a grouped convolution ensemble network (GCE-Net) that enables the fusion of features extracted from original and attention-guided ensemble models.The performance of person re-id using WSE-Net was evaluated on the Dongguk body-based person recognition database version 1 (DBPerson-Recog-DB1) and the Sun Yat-sen university multiple modality re-identification version 1 (SYSU-MM01). Experimental results demonstrated that on DBPerson-Recog-DB1, WSE-Net achieved 93.65% in rank 1, 95.28% in mean average precision (mAP), and 93.52% in the harmonic mean of precision and recall. Additionally, on SYSU-MM01, WSE-Net achieved 86.85% in rank 1, 44.58% in mAP, and 40.06% in the harmonic mean of precision and recall. Furthermore, the accuracy of WSE-Net on both datasets surpassed that of state-of-the-art methods.

TransImg: A Translation Algorithm of Visible-to-Infrared Image Based on Generative Adversarial Network

Infrared images of sensitive targets are difficult to obtain and cannot meet the design and training needs of target detection and tracking algorithms for mobile platforms such as aircraft. This paper proposes an image translation algorithm TransImg, which can achieve visible light image translation to the infrared domain to enrich the dataset. First, the algorithm designed a generator structure consisting of a deep residual connected encoder and a region perception feature fusion module to enhance feature learning, thereby avoiding issues such as generating infrared images with insufficient details in the transfer task. Afterward, a multi-scale discriminator and a composite loss function were designed to further improve the transfer effect. Finally, an automatic mixed-precision training strategy was designed for the overall migration algorithm architecture to accelerate the training and generation of infrared images. Experiments have shown that the image translation algorithm TransImg has good algorithm accuracy, and the infrared image generated by visible light image translation has richer texture details, faster generation speed, and lower video memory consumption, and the performance exceeds the mainstream traditional algorithm, and the generated images can meet the requirements of target detection and tracking algorithms design and training for mobile platforms such as aircraft.

Coumarin-Based ACQ-AIE Conversion Photosensitizer for Mitochondrial Imaging and Synergistic Cancer Therapy.

Most of the traditional fluorescent molecules have the advantages of high fluorescence quantum yield, good stability, and excellent structural adjustability, but they exhibit the characteristics of fluorescence quenching caused by aggregation, which restricts their application in aqueous solutions or solids. The excellent luminescence properties and photosensitive potential of aggregation-induced emission (AIE) materials in a condensed state have made them widely concerned in the scientific research field, so it is very challenging to regulate the transformation of traditional aggregation-caused quenching (ACQ) fluorophores into AIE fluorophores. In this study, the traditional coumarin fluorophore was used as a matrix. After conjugating the triphenylamine AIE group, the triphenylphosphine cation was linked through the alkyl chain to obtain a molecular probe NCTPP with excellent AIE characteristic, water solubility, mitochondrial green light imaging, chemotherapy and photodynamic therapy capabilities. As far as we know, it was the first time that the photosensitivity of coumarin fluorescent molecules was imparted by the ACQ-AIE conversion method.

Halamphorahampyeongensis sp. nov. (Amphipleuraceae, Bacillariophyceae), a new marine benthic diatom from a tidal mudflat in Hampyeong Bay, South Korea.

The abundance and variety of benthic diatoms inhabiting tidal flats is widely acknowledged, although it has received relatively less attention than other research areas. In this investigation, we provide a formal description of a benthic diatom found in the tidal mudflat of South Korea, based on morphological and molecular characteristics and the similarities and differences between Halamphorahampyeongensis sp. nov., with morphologically similar Halamphora species are also discussed. Morphological characteristics are described from light and electron microscopy images. H.hampyeongensis is distinguished by its wide ventral sides of the valve, small and rounded areolae present across the whole valve face, and dense dorsal striae biseriate (34-38 in 10 μm). Phylogenetic analysis based on 18S rDNA and rbcL sequence data revealed that H.hampyeongensis is related to H.montana, H.mosensis, and H.specensa. The results (morphometric and molecular) provide sufficient elements to support and propose this taxon as a new species.

LLD-GAN: An end-to-end network for low-light image demosaicking

Demosaicking of low and ultra-low light images has wide applications in the fields of consumer electronics, security, and industrial machine vision. Denoising is a challenge in the demosaicking process. This study introduces a comprehensive end-to-end low-light demosaicking framework called LLD-GAN (Low Light Demosaicking Generative Adversarial Network), which greatly reduces the computational complexity. Our architecture employs a Wasserstein GAN framework enhanced by a gradient penalty mechanism. We have redesigned the generator based on the UNet++ network as well as its corresponding discriminator, which makes the model learning more efficient. In addition, we propose a new loss metric grounded in the principles of perceptual loss to obtain images with better visual quality. The contribution of Wasserstein GAN with gradient penalty and perceptual loss function was proved to be beneficial by our ablation experiments. For RGB images, we tested the proposed model under a wide range of low light levels, from 1/30 to 1/150 of normal light level, for 16-bit images with added noise. For actual low-light raw sensor images, the model was evaluated under three distinct lighting conditions: 1/100, 1/250, and 1/300 of normal exposure. The qualitative and quantitative comparison against advanced techniques demonstrates the validity and superiority of the LLD-GAN as a unified denoising-demosaicking tool.

Frugal engineering-inspired wearable augmented reality goggle system enables fluorescence-guided cancer surgery

Disparities in surgical outcomes often result from subjective decisions dictated by surgical training, experience, and available resources. To improve outcomes, surgeons have adopted advancements in robotics, endoscopy, and intra-operative imaging including fluorescence-guided surgery (FGS), which highlights tumors and anatomy in real-time. However, technical, economic, and logistic challenges hinder widespread adoption of FGS beyond high-resource centers. To overcome these impediments, we combined laser diodes, Raspberry Pi cameras and computers, off-the-shelf optical components, and 3D-printed parts to make a battery-powered, compact, dual white light and NIR imaging system that has comparable performance to existing bulkier, pricier, and wall-powered technologies. We combined these components with off-the-shelf augmented reality (AR) glasses to create a fully-wearable fluorescence imaging AR Raspberry Pi-based goggle system (FAR-Pi) and validated performance in a pre-clinical cancer surgery model. Novel device design ensures distance-independent coalignment between real and augmented views. As an open-source, affordable, and adaptable system, FAR-Pi is poised to democratize access to FGS and improve health outcomes worldwide.

Polarization-Enhanced Underwater Laser Range-Gated Imaging for Subaquatic Applications.

Laser range-gated underwater imaging technology, by removing most of the backscattering noise, can effectively increase image contrast and extend the detection range. The optical signal captured by a range-gated imaging system primarily comprises reflected light from the object and backscattered light from the surrounding water. Consequently, surfaces with low reflectivity or highly turbid water environments substantially constrain the applicability of the range-gated imaging system. To enhance the detection capability of underwater laser range-gated imaging, this paper proposes the incorporation of underwater polarized light imaging technology as an enhancement method. Based on polarization differences, backscattered light and reflected light from an object can be distinguished. Experimental results indicate that, compared to images obtained using a conventional range-gated laser imaging system, those captured with a polarization-enhanced system exhibit an increase of up to 47% for the corresponding Enhancement Measure Evaluation (EME) index. The proposed approach, which integrates polarization imaging with range-gated laser imaging, has the potential to broaden the applicability of underwater laser imaging scenarios, such as deep-sea exploration and military applications.

Lightweight Infrared Image Denoising Method Based on Adversarial Transfer Learning.

A lightweight infrared image denoising method based on adversarial transfer learning is proposed. The method adopts a generative adversarial network (GAN) framework and optimizes the model through a phased transfer learning strategy. In the initial stage, the generator is pre-trained using a large-scale grayscale visible light image dataset. Subsequently, the generator is fine-tuned on an infrared image dataset using feature transfer techniques. This phased transfer strategy helps address the problem of insufficient sample quantity and variety in infrared images. Through the adversarial process of the GAN, the generator is continuously optimized to enhance its feature extraction capabilities in environments with limited data. Moreover, the generator structure incorporates structural reparameterization technology, edge convolution modules, and progressive multi-scale attention block (PMAB), significantly improving the model's ability to recognize edge and texture features. During the inference stage, structural reparameterization further optimizes the network architecture, significantly reducing model parameters and complexity and thereby improving denoising efficiency. The experimental results of public and real-world datasets demonstrate that this method effectively removes additive white Gaussian noise from infrared images, showing outstanding denoising performance.

Correlative microscopy - illuminating the endomembrane system of plant seeds.

The endomembrane system of cereal seed endosperm is a highly plastic and dynamic system reflecting the high degree of specialization of this tissue. It is capable of coping with high levels of storage protein synthesis and undergoes rapid changes to accommodate these storage proteins in newly formed storage organelles such as endoplasmic reticulum-derived protein bodies or protein storage vacuoles. The study of endomembrane morphology in cereal endosperm is challenging due to the amount of starch that cereal seeds accumulate and the progressive desiccation of the tissue. Here, we present a comprehensive study of the endomembrane system of developing barley endosperm cells, complemented by correlative light and electron microscopy (CLEM) imaging. The use of genetically fused fluorescent protein tags in combination with the high resolution of electron microscopy brings ultrastructural research to a new level and can be used to generate novel insights in cell biology in general and in cereal seed research in particular.

LFIR-YOLO: Lightweight Model for Infrared Vehicle and Pedestrian Detection

The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared imagery, we propose an enhanced infrared detection model called LFIR-YOLO, which is built upon the YOLOv8 architecture. The primary goal is to improve the accuracy of infrared target detection in nighttime traffic scenarios while meeting practical deployment requirements. First, to address challenges such as limited contrast and occlusion noise in infrared images, the C2f module in the high-level backbone network is augmented with a Dilation-wise Residual (DWR) module, incorporating multi-scale infrared contextual information to enhance feature extraction capabilities. Secondly, at the neck of the network, a Content-guided Attention (CGA) mechanism is applied to fuse features and re-modulate both initial and advanced features, catering to the low signal-to-noise ratio and sparse detail features characteristic of infrared images. Third, a shared convolution strategy is employed in the detection head, replacing the decoupled head strategy and utilizing shared Detail Enhancement Convolution (DEConv) and Group Norm (GN) operations to achieve lightweight yet precise improvements. Finally, loss functions, PIoU v2 and Adaptive Threshold Focal Loss (ATFL), are integrated into the model to better decouple infrared targets from the background and to enhance convergence speed. The experimental results on the FLIR and multispectral datasets show that the proposed LFIR-YOLO model achieves an improvement in detection accuracy of 4.3% and 2.6%, respectively, compared to the YOLOv8 model. Furthermore, the model demonstrates a reduction in parameters and computational complexity by 15.5% and 34%, respectively, enhancing its suitability for real-time deployment on resource-constrained edge devices.