Dual-polarization defogging method based on frequency division and blind separation of polarization information.

The current advancements in image processing have led to significant progress in polarization defogging methods. However, most existing approaches are not suitable for scenes with targets exhibiting a high degree of polarization (DOP), as they rely on the assumption that the detected polarization information solely originates from the airlight. In this paper, a dual-polarization defogging method connecting frequency division and blind separation of polarization information is proposed. To extract the polarization component of direct transmission light from the detected polarized signal, blind separation of overlapped polarized information is performed in the low-frequency domain based on visual perception. Subsequently, after estimating airlight, a high-quality defogging image can be restored. Extensive experiments conducted on real-world scenes and comparative tests confirm the superior performance of our proposed method compared to other competitive methods, particularly in reconstructing objects with high DOP. This work provides a quantitative approach for estimating the contributions of polarization light from different sources and further expands the application range of polarimetric defogging imaging.

Bio-inspired foveal super-resolution method for multi-focal-length images based on local gradient constraints.

Most existing super-resolution (SR) imaging systems, inspired by the bionic compound eye, utilize image registration and reconstruction algorithms to overcome the angular resolution limitations of individual imaging systems. This article introduces a multi-aperture multi-focal-length imaging system and a multi-focal-length image super-resolution algorithm, mimicking the foveal imaging of the human eye. Experimental results demonstrate that with the proposed imaging system and an SR imaging algorithm inspired by the human visual system, the proposed method can enhance the spatial resolution of the foveal region by up to 4 × compared to the original acquired image. These findings validate the effectiveness of the proposed imaging system and computational imaging algorithm in enhancing image texture and spatial resolution.

Joint constraints of guided filtering based confidence and nonlocal sparse tensor for color polarization super-resolution imaging.

This paper introduces a camera-array-based super-resolution color polarization imaging system designed to simultaneously capture color and polarization information of a scene in a single shot. Existing snapshot color polarization imaging has a complex structure and limited generalizability, which are overcome by the proposed system. In addition, a novel reconstruction algorithm is designed to exploit the complementarity and correlation between the twelve channels in acquired color polarization images for simultaneous super-resolution (SR) imaging and denoising. We propose a confidence-guided SR reconstruction algorithm based on guided filtering to enhance the constraint capability of the observed data. Additionally, by introducing adaptive parameters, we effectively balance the data fidelity constraint and the regularization constraint of nonlocal sparse tensor. Simulations were conducted to compare the proposed system with a color polarization camera. The results show that color polarization images generated by the proposed system and algorithm outperform those obtained from the color polarization camera and the state-of-the-art color polarization demosaicking algorithms. Moreover, the proposed algorithm also outperforms state-of-the-art SR algorithms based on deep learning. To evaluate the applicability of the proposed imaging system and reconstruction algorithm in practice, a prototype was constructed for color polarization image acquisition. Compared with conventional acquisition, the proposed solution demonstrates a significant improvement in the reconstructed color polarization images.

Multispectral image defogging based on a wavelength-dependent extinction coefficient model in fog.

Most of the state-of-the-art defogging models presented in the literature assume that the attenuation coefficient of all spectral channels is constant, which inevitably leads to spectral distortion and information bias. To address this issue, this paper proposes a defogging method that takes into account the difference between the extinction coefficients of multispectral channels of light traveling through fog. Then the spatially distributed transmission map of each spectral channel is reconstructed to restore the fog-degraded images. The experimental results of various realistic complex scenes show that the proposed method has more outstanding advantages in restoring lost detail, compensating for degraded spectral information, and recognizing more targets hidden in uniform ground fog than state-of-the-art technologies. In addition, this work provides a method to characterize the intrinsic property of fog expressed as multispectral relative extinction coefficients, which act as a fundament for further reconstruction of multispectral information.

Enhancing Infrared Optical Flow Network Computation through RGB-IR Cross-Modal Image Generation.

Due to the complexity of real optical flow capture, the existing research still has not performed real optical flow capture of infrared (IR) images with the production of an optical flow based on IR images, which makes the research and application of deep learning-based optical flow computation limited to the field of RGB images only. Therefore, in this paper, we propose a method to produce an optical flow dataset of IR images. We utilize the RGB-IR cross-modal image transformation network to rationally transform existing RGB image optical flow datasets. The RGB-IR cross-modal image transformation is based on the improved Pix2Pix implementation, and in the experiments, the network is validated and evaluated using the RGB-IR aligned bimodal dataset M3FD. Then, RGB-IR cross-modal transformation is performed on the existing RGB optical flow dataset KITTI, and the optical flow computation network is trained using the IR images generated by the transformation. Finally, the computational results of the optical flow computation network before and after training are analyzed based on the RGB-IR aligned bimodal data.

Spatial and Channel Aggregation Network for Lightweight Image Super-Resolution.

Advanced deep learning-based Single Image Super-Resolution (SISR) techniques are designed to restore high-frequency image details and enhance imaging resolution through the use of rapid and lightweight network architectures. Existing SISR methodologies face the challenge of striking a balance between performance and computational costs, which hinders the practical application of SISR methods. In response to this challenge, the present study introduces a lightweight network known as the Spatial and Channel Aggregation Network (SCAN), designed to excel in image super-resolution (SR) tasks. SCAN is the first SISR method to employ large-kernel convolutions combined with feature reduction operations. This design enables the network to focus more on challenging intermediate-level information extraction, leading to improved performance and efficiency of the network. Additionally, an innovative 9 × 9 large kernel convolution was introduced to further expand the receptive field. The proposed SCAN method outperforms state-of-the-art lightweight SISR methods on benchmark datasets with a 0.13 dB improvement in peak signal-to-noise ratio (PSNR) and a 0.0013 increase in structural similarity (SSIM). Moreover, on remote sensing datasets, SCAN achieves a 0.4 dB improvement in PSNR and a 0.0033 increase in SSIM.

Three Dimensional Shape Reconstruction via Polarization Imaging and Deep Learning.

Deep-learning-based polarization 3D imaging techniques, which train networks in a data-driven manner, are capable of estimating a target's surface normal distribution under passive lighting conditions. However, existing methods have limitations in restoring target texture details and accurately estimating surface normals. Information loss can occur in the fine-textured areas of the target during the reconstruction process, which can result in inaccurate normal estimation and reduce the overall reconstruction accuracy. The proposed method enables extraction of more comprehensive information, mitigates the loss of texture information during object reconstruction, enhances the accuracy of surface normal estimation, and facilitates more comprehensive and precise reconstruction of objects. The proposed networks optimize the polarization representation input by utilizing the Stokes-vector-based parameter, in addition to separated specular and diffuse reflection components. This approach reduces the impact of background noise, extracts more relevant polarization features of the target, and provides more accurate cues for restoration of surface normals. Experiments are performed using both the DeepSfP dataset and newly collected data. The results show that the proposed model can provide more accurate surface normal estimates. Compared to the UNet architecture-based method, the mean angular error is reduced by 19%, calculation time is reduced by 62%, and the model size is reduced by 11%.

An Automatic Calibration Method for the Field of View Aberration in a Risley-Prism-Based Image Sensor.

Risley-prism-based image sensors can expand the imaging field of view through beam control. The larger the top angle of the prism, the higher the magnification of the field of view, but at the same time, it aggravates the problem of imaging aberrations, which also puts higher requirements on the aberration correction method for the Risley-prism-based image sensor. To improve the speed, accuracy, and stability of the aberration correction process, an automatic calibration method for the Risley-prism-based image sensor is proposed based on a two-axis turntable. The image datasets of the calibration plate with different prism rotation angles and object distances are acquired using a two-axis turntable. Then, the images of the calibration plate are pre-processed using the bicubic interpolation algorithm. The calibration parameters are finally calculated, and parameter optimization is performed. The experimental results verify the feasibility of this automated calibration method. The reprojection error of the calibration is within 0.26 pixels when the distance of the imaging sensor is 3.6 m from the object, and the fine aberration correction results are observed.

Diversity of endophytic bacteria in hybrid maize seeds and Bacillus mojavensis J2416-7 may be capable of vertical transmission.

The diversity of endophytic bacteria in the progeny is related to the parental lines. In this study, the traditional separation method was used to study the dominant endophytic bacteria of the shared paternal line and its pollen, different maternal lines and their F1 progeny. And the results showed that the dominant endophytic bacteria in maize seeds and the pollen were Bacillus and Pantoea. The Bacillus diversity of the progeny JMC121 and JN728 were the same as both the paternal line and the maternal line, including Bacillus subtilis, Bacillus velezensis, Bacillus mojavensis, and Bacillus licheniformis. The Bacillus subtilis and Bacillus velezensis in JN828 were the same as both the paternal line and the maternal line, while Bacillus licheniformis was only the same as the paternal line. Through the RAPD molecular typing, there was the same strain of Bacillus mojavensis existed in the paternal line J2416, the pollen and the progeny JN728; this meant that the paternal line passed its dominant endophytic bacteria to the progeny through pollen in vertical transmission. This study showed that the dominant endophytic bacteria in maize seeds and the pollen were Bacillus, and the diversity of F1 progeny was related to both the paternal line and the maternal line.

Comparison of High-Speed Polarization Imaging Methods for Biological Tissues.

We applied a polarization filter array and high-speed camera to the imaging of biological tissues during large, dynamic deformations at 7000 frames per second. The results are compared to previous measurements of similar specimens using a rotating polarizer imaging system. The polarization filter eliminates motion blur and temporal bias from the reconstructed collagen fiber alignment angle and retardation images. The polarization imaging configuration dose pose additional challenges due to the need for calibration of the polarization filter array for a given sample in the same lighting conditions as during the measurement.

Flexible foveated imaging using a single Risley-prism imaging system.

Foveated imaging, which has the ability to provide overall situational awareness over a large field of view and high-resolution perception of local details, has significant advantages in many specific applications. However, existing artificially foveated imaging systems are complex, bulky, and expensive, and the flexibility of the fovea specifically has many limitations. To overcome these deficiencies, this paper proposes a method for foveated imaging by collecting multiple partially overlapping sub-fields of view. To capture the above special sub-fields of view, we propose a high-efficiency algorithm based on the characteristics of the field of view deflected by the Risley-prism and aimed at solving the prism rotation angles. In addition, we prove the reliability of the proposed algorithm by cross-validation with the particle swarm optimization algorithm. The experimental results show that the proposed method can achieve flexible foveated imaging using a single Risley-prism imaging system.

Diversity, enzyme production and antibacterial activity of Bacillus strains isolated from sesame-flavored liquor Daqu.

Daqu provides enzymes and precursors for liquor fermentation, and is the core of liquor fermentation. In this study, 11 Bacillus strains were isolated from sesame-flavored liquor Daqu, which can not only produce protease and amylase, but also have antagonistic effects on common pathogens Escherichia coli and Staphylococcus aureus. According to the gyrA gene phylogeny analysis, these 11 Bacillus strains belong to three species, B1, Y14, Y15, and YPDW9 belong to Bacillus mojavensis, W7, W13, YPDW6, and YPDW12 belong to Bacillus subtilis, and W14, Y5, and YPDW1 belong to Bacillus velezensis. According to the results of random amplified polymorphic DNA (RAPD) typing, there are three strains in Bacillus mojavensis, among which Y14 and Y15 are the same ones. All four Bacillus subtilis strains and three Bacillus velezensis strains are different. The specific primers were used to randomly amplify the biological control genes expressing lipopeptide antibiotics (bioA, bmyB, ituC, fenD, srfAA, srfAB, yngG,and yndJ), and the results showed that antagonistic genes other than fenD gene were amplified in four Bacillus mojavensis strains; Bacillus subtilis amplification was significantly different, but srfAA, bmyB and yndJ genes were all present; All genes were amplified in Bacillus velezensis except YPDW1 without ituC. This research provides new ideas for strengthening Daqu and lays a foundation for improving the quality of liquor.

High-throughput sequencing-based analysis of the composition and diversity of endophytic bacterial community in seeds of upland rice.

Upland rice is an ecotype crop resulting from the long-term domestication and evolution of rice in dry land without a water layer. Generally, the stems and leaves are thick and luxuriant, while the leaves also typically broad and light. The root system is developed with abundant root hair, and the osmotic pressure of the root and cell juice concentration in the leaves is high, while this plant is drought-resistant, heat-resistant, and water absorbent. This study aims to reveal the "core flora" of the endophytes in upland rice seeds by examining their diversity and community structures. It further intends to reveal the impact of the soil environment on the formation of endophyte community structures in upland rice seeds by comparing the environmental soil microorganisms in upland rice habitats. In this study, high-throughput sequencing technology based on the Illumina Hiseq 2500 platform was used to investigate the structure and diversity of endophytic bacterial communities using upland rice varieties collected from different locations and soil samples from unified planting sites as materials. Here, 42 endophytic OTUs were found to coexist in the 14 samples. At the phylum level, the first dominant phyla in all the samples were Proteobacteria (93.81-99.99%). At the genus level, Pantoea (8.77-87.77%), Pseudomonas (1.15-61.58%), Methylobacterium (0.40-4.64%), Sphingomonas (0.26-3.85%), Microbacterium (0.01-4.67%) and Aurantimonas (0.04-4.34%), which represent the core microflora in upland rice seeds, served as the dominant genera that coexisted in all the upland rice seeds tested. This study significant for the isolation, screening, functional evaluation, and re-action of various functional microorganisms in upland rice to improve its agronomic traits. It also provides a specific reference for the interaction between microorganisms and plants.

Polarization dehazing method based on spatial frequency division and fusion for a far-field and dense hazy image.

Polarization dehazing technology is effective in imaging through scattering media because of additional information different from the light intensity and spectrum. However, the existing methods relying on the manual choice of bias factor are non-universal in different imaging conditions. In addition, these methods are not suitable for dense scenes with long distances. Aiming at the dehazing application requirements in far-field and dense hazy weather, a polarization dehazing method based on spatial frequency division and fusion (SFDF) is proposed in this paper. In addition, we optimize the interpolation process before dehazing so that the spatial resolution can be maintained without the noise influence. The experimental results indicate that the proposed method outperforms the existing schemes in dense hazy weather more than kilometer distances. Furthermore, we discuss that the effects of bias factors only act on the low-frequency parts of the polarization images, and their influence is greatly weakened after being fused with the high-frequency parts. This robust advantage without manual intervention causes the proposed SFDF method to have a broader prospect in practical application scenarios.

An endophytic strain of the genus Bacillus isolated from the seeds of maize (Zea mays L.) has antagonistic activity against maize pathogenic strains.

Plant endophytes are microbes that colonize plant internal tissues and are ubiquitously associated with plants. In this study, seven endophytic bacterial strains, 665L2, 725L2, 725R2, 92R2, 728R3, 728R4 and 2416T3, were isolated from seeds of five healthy maize varieties (Zea mays L.) and all identified as Bacillus velezensis by polyphasic taxonomy based on 16S rRNA and gyrA gene phylogenetic analysis. In addition, according to the genotyping results from random amplified polymorphic DNA (RAPD), 665L2, 725L2, 725R2 and 92R2 belonged to the same strain, while 728R3 and 2416T3 belonged to another strain. Pathogenic fungal strains 4, 5 and 6 were isolated from three diseased maize varieties (Zea mays L.), and they were identified as Talaromyces funiculosus, Penicillium oxalicum and Fusarium verticillioides, respectively, by polyphasic taxonomy based on morphological identification, ITS rDNA and bio-control gene phylogenetic analyses. Seven endophytic bacterial Bacillus velezensis strains had favourable antagonistic activity, and antagonistic testing was carried out against the three pathogenic strains, Talaromyces funiculosus 4, Penicillium oxalicum 5 and Fusarium verticillioides 6. Biological control lipopeptide antibiotic genes (bioA, bmyB, ituC, fenD, srfAA, srfAB, yngG and yndJ) were amplified using specific primers, and they were found in the seven endophytic bacterial Bacillus velezensis strains. This study provides a scientific basis for future research on the use of resistant endophytic bacterial resources to enhance crop production.

Complete genome sequence of Bacillus ciccensis 5L6T, a new species isolated from maize (Zea mays L.) seeds with anti-fungal activity.

Maize is a crop grown worldwide. Bacillus ciccensis 5L6T was isolated from maize (Zea mays L., Jingke968) seeds and was a new species of Bacillus with potential anti-fungal activity. The complete genome of 5L6T was sequenced and assembled with a length of 5,207,802 bp and a GC content of 37.42%. The proteins responsible for anti-fungal activity and the potential beneficial interaction with maize of 5L6Twere annotated and reported here. The complete genome sequence of the new species B. ciccensis 5L6T will promote its biological application.

Spectral Clustering Super-Resolution Imaging Based on Multispectral Camera Array.

Although multispectral and hyperspectral imaging acquisitions are applied in numerous fields, the existing spectral imaging systems suffer from either low temporal or spatial resolution. In this study, a new multispectral imaging system-camera array based multispectral super resolution imaging system (CAMSRIS) is proposed that can simultaneously achieve multispectral imaging with high temporal and spatial resolutions. The proposed registration algorithm is used to align pairs of different peripheral and central view images. A novel, super-resolution, spectral-clustering-based image reconstruction algorithm was developed for the proposed CAMSRIS to improve the spatial resolution of the acquired images and preserve the exact spectral information without introducing false information. The reconstructed results showed that the spatial and spectral quality and operational efficiency of the proposed system are better than those of a multispectral filter array (MSFA) based on different multispectral datasets. The PSNR of the multispectral super-resolution images obtained by the proposed method were respectively higher by 2.03 and 1.93 dB than those of GAP-TV and DeSCI, and the execution time was significantly shortened by approximately 54.55 s and 9820.19 s when the CAMSI dataset was used. The feasibility of the proposed system was verified in practical applications based on different scenes captured by the self-built system.

The relationship between atrial fibrillation and NLRP3 inflammasome: a gut microbiota perspective.

Atrial fibrillation (AF) is a common clinical arrhythmia whose pathogenesis has not been fully elucidated, and the inflammatory response plays an important role in the development of AF. The inflammasome is an important component of innate immunity and is involved in a variety of pathophysiologic processes. The NLRP3 inflammasome is by far the best studied and validated inflammasome that recognizes multiple pathogens through pattern recognition receptors of innate immunity and mediates inflammatory responses through activation of Caspase-1. Several studies have shown that NLRP3 inflammasome activation contributes to the onset and development of AF. Ecological dysregulation of the gut microbiota has been associated with the development of AF, and some evidence suggests that gut microbiota components, functional byproducts, or metabolites may induce or exacerbate the development of AF by directly or indirectly modulating the NLRP3 inflammasome. In this review, we report on the interconnection of NLRP3 inflammasomes and gut microbiota and whether this association is related to the onset and persistence of AF. We discuss the potential value of pharmacological and dietary induction in the management of AF in the context of the association between the NLRP3 inflammasome and gut microbiota. It is hoped that this review will lead to new therapeutic targets for the future management of AF.

Microbial composition and dynamic succession during the Daqu production process of Northern Jiang-flavored liquor in China.

The microbial community structure and succession regularity of six key periods during high-temperature Daqu production were revealed using high-throughput sequencing to explore the factors affecting the flavor formation of Northern Jiang-flavored Baijiu technology. The results showed that among the six Daqu samples, the bacteria mainly included Firmicutes, Actinobacteriota, and Proteobacteria, of which Proteobacteria was the most dominant. The primary fungus was Ascomycota. At the genus level, the primary bacterial groups were Lactobacillus, Weissella, Bacillus, Delftia, Achromobacter, Saccharopolyspora, Thermoactinomyces, Scopulibacillus, Pseudomonas, and Stenotrophomonas. The main fungal groups in the Daqu were Wickerhamomyces, Saccharomycopsis, Thermoascus, and Thermomyces. During the initial stage of Daqu production, the dominant bacteria were Lactobacillus (20.07%) and Weissella (48.30%). As the fermentation temperature of the Daqu increased, Achromobacter, Stenotrophomonas, and Delftia became the dominant bacteria during the first Daqu flipping period, the second Daqu flipping period, and the dry-fire period. During these three periods, many bacteria were eliminated, decreasing the bacterial diversity, while a decline in temperature was evident during the Daqu exit period. After adapting to the high-temperature environment, the accumulation of Saccharopolyspora (22.07%), Thermoactinomyces (16.73%), Scopulibacillus (27.13%), Kroppenstedtia (9.03%), and Bacillus (6.97%) increased the bacterial diversity during the Daqu exit period. Wickerhamomyces (83.47%) represented the main dominant fungus during the initial production stage but were eliminated with increased temperature. Furthermore, a higher temperature increased the abundance of Saccharomycopsis and Thermoascus, while Thermomyces gradually accumulated in the D, E, and F samples. Thermomyces (79.90%) and Thermoascus (13.83%) became the dominant fungi during the Daqu exit period. In this study, high-throughput sequencing technology was used to reveal the microbial diversity during the high-temperature Daqu production process of Northern Jiang-flavored Baijiu. This provided a scientific basis for improving the production process of this product in the future. Therefore, understanding the formation of the flavor substances and the related microorganisms in Northern Jiang-flavored Baijiu can provide guidance for using them to manipulate the preparation process while implementing microbial control and improving the production procedures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-02779-8.

High-throughput sequencing of the microbial diversity of roasted-sesame-like flavored Daqu with different characteristics.

The purpose of this experiment was to analyze the microbial community diversity in three Daqu samples displaying different characteristics in the same Daqu fermentation chamber. A high throughput sequencing technique was used to detect the microbial abundance and diversity in these Daqu samples. Of the three samples, the microbial diversity in the Black sample (sample B) was significantly higher than in the other two. At the genus level, Saccharopolyspora, Bacillus, Lentibacillus, Staphylococcus, Kroppenstedtia, and Thermoactinomyces were the primary bacterial groups in the sesame-flavored liquor, while Thermomyces, Thermoascus, and Aspergillus represented the main fungal groups. In sample B, the dominant bacteria were Thermoactinomyces, Saccharopolyspora, and Pseudomonas. In the White sample (sample W), Thermoactinomyces was the most abundant, followed by Saccharopolyspora and Lentibacillus. Staphylococcus dominated in the Yellow sample (sample Y), followed by Bacillus and Kroppenstedtia. Regarding the fungi in the three samples, Thermomyces accounted for 93.70% in sample B, and Aspergillus dominated in sample W, while the Thermoascus and Aspergillus content were similar in the sample Y. This study examined the microbial diversity in liquor Daqu with different sesame flavors, providing a foundation for microbial regulation, while investigating the relationship between flavored liquor compounds and microorganisms.