Low illumination fog noise image denoising method based on ACE-GPM.

The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image's foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality.

Color constancy mechanisms in virtual reality environments.

Prior research has demonstrated high levels of color constancy in real-world scenarios featuring single light sources, extensive fields of view, and prolonged adaptation periods. However, exploring the specific cues humans rely on becomes challenging, if not unfeasible, with actual objects and lighting conditions. To circumvent these obstacles, we employed virtual reality technology to craft immersive, realistic settings that can be manipulated in real time. We designed forest and office scenes illuminated by five colors. Participants selected a test object most resembling a previously shown achromatic reference. To study color constancy mechanisms, we modified scenes to neutralize three contributors: local surround (placing a uniform-colored leaf under test objects), maximum flux (keeping the brightest object constant), and spatial mean (maintaining a neutral average light reflectance), employing two methods for the latter: changing object reflectances or introducing new elements. We found that color constancy was high in conditions with all cues present, aligning with past research. However, removing individual cues led to varied impacts on constancy. Local surrounds significantly reduced performance, especially under green illumination, showing strong interaction between greenish light and rose-colored contexts. In contrast, the maximum flux mechanism barely affected performance, challenging assumptions used in white balancing algorithms. The spatial mean experiment showed disparate effects: Adding objects slightly impacted performance, while changing reflectances nearly eliminated constancy, suggesting human color constancy relies more on scene interpretation than pixel-based calculations.

A Retinex-based network for image enhancement in low-light environments.

Most of the existing low-light image enhancement methods suffer from the problems of detail loss, color distortion and excessive noise. To address the above-mentioned issues, this paper proposes a neural network-based low-light image enhancement network. The network is divided into three parts: decomposition network, reflection component denoising network, and illumination component enhancement network. In the decomposition network, the input image is decomposed into a reflection image and an illumination image. In the reflection component denoising network, the Unet3+ network improved by fusion CA attention is adopted to denoise the reflection image. In the illumination component enhancement network, the adaptive mapping curve is adopted to enhance the illumination image iteratively. Finally, the processed illumination and reflection images are fused based on Retinex theory to obtain the final enhanced image. The experimental results show that the proposed network achieves excellent visual effects in subjective evaluation. Additionally, it shows a significant improvement in objective evaluation metrics, including PSNR, SSIM, NIQE, and so on, when compared to the results in several public datasets.

High correlated color temperature artificial lighting impairs retinal pigment epithelium integrity and chloride ion transport: A potential mechanism for choroidal thinning.

Among the environmental factors contributing to myopia, the role of correlated color temperature (CCT) of ambient light emerges as a key element warranting in-depth investigation. The choroid, a highly vascularized and dynamic structure, often undergoes thinning during the progression of myopia, though the precise mechanism remains elusive. The retinal pigment epithelium (RPE), the outermost layer of the retina, plays a pivotal role in regulating the transport of ion and fluid between the subretinal space and the choroid. A hypothesis suggests that variations in choroidal thickness (ChT) may be modulated by transepithelial fluid movement across the RPE. Our experimental results demonstrate that high CCT illumination significantly compromised the integrity of tight junctions in the RPE and disrupted chloride ion transport. This functional impairment of the RPE may lead to a reduction in fluid transfer across the RPE, consequently resulting in choroidal thinning and potentially accelerating axial elongation. Our findings provide support for the crucial role of the RPE in regulating ChT. Furthermore, we emphasize the potential hazards posed by high CCT artificial illumination on the RPE, the choroid, and refractive development, underscoring the importance of developing eye-friendly artificial light sources to aid in the prevention and control of myopia.

Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion.

The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human-computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions of these key points. While the Mediapipe algorithm demonstrates effective anti-shake capabilities for these points, its accuracy can be easily affected by changes in lighting conditions. To address these challenges, this study proposes an illumination-adaptive algorithm for detecting human key points through the fusion of multi-source information. By integrating key point data from the depth camera and Mediapipe, an illumination change model is established to simulate environmental lighting variations. Subsequently, the fitting function of the relationship between lighting conditions and adaptive weights is solved to achieve lighting adaptation for human key point detection. Experimental verification and similarity analysis with benchmark data yielded R2 results of 0.96 and 0.93, and cosine similarity results of 0.92 and 0.90. With a threshold range of 8, the joint accuracy rates for the two rehabilitation actions were found to be 89% and 88%. The experimental results demonstrate the stability of the proposed method in detecting key points in the human body under changing illumination conditions, its anti-shake ability for human movement, and its high detection accuracy. This method shows promise for applications in human-computer interaction, sports rehabilitation, and virtual reality.

Improving strawberry plant resilience to salinity and alkalinity through the use of diverse spectra of supplemental lighting.

BACKGROUND: This study explores the impact of various light spectra on the photosynthetic performance of strawberry plants subjected to salinity, alkalinity, and combined salinity/alkalinity stress. We employed supplemental lighting through Light-emitting Diodes (LEDs) with specific wavelengths: monochromatic blue (460 nm), monochromatic red (660 nm), dichromatic blue/red (1:3 ratio), and white/yellow (400-700 nm), all at an intensity of 200 µmol m-2 S-1. Additionally, a control group (ambient light) without LED treatment was included in the study. The tested experimental variants were: optimal growth conditions (control), alkalinity (40 mM NaHCO3), salinity (80 mM NaCl), and a combination of salinity/alkalinity. RESULTS: The results revealed a notable decrease in photosynthetic efficiency under both salinity and alkalinity stresses, especially when these stresses were combined, in comparison to the no-stress condition. However, the application of supplemental lighting, particularly with the red and blue/red spectra, mitigated the adverse effects of stress. The imposed stress conditions had a detrimental impact on both gas exchange parameters and photosynthetic efficiency of the plants. In contrast, treatments involving blue, red, and blue/red light exhibited a beneficial effect on photosynthetic efficiency compared to other lighting conditions. Further analysis of JIP-test parameters confirmed that these specific light treatments significantly ameliorated the stress impacts. CONCLUSIONS: In summary, the utilization of blue, red, and blue/red light spectra has the potential to enhance plant resilience in the face of salinity and alkalinity stresses. This discovery presents a promising strategy for cultivating plants in anticipation of future challenging environmental conditions.

Selective-plane-activation structured illumination microscopy.

The background light from out-of-focus planes hinders resolution enhancement in structured illumination microscopy when observing volumetric samples. Here we used selective plane illumination and reversibly photoswitchable fluorescent proteins to realize structured illumination within the focal plane and eliminate the out-of-focus background. Theoretical investigation of the imaging properties and experimental demonstrations show that selective plane activation is beneficial for imaging dense microstructures in cells and cell spheroids.

Open-3DSIM: an open-source three-dimensional structured illumination microscopy reconstruction platform.

Open-3DSIM is an open-source reconstruction platform for three-dimensional structured illumination microscopy. We demonstrate its superior performance for artifact suppression and high-fidelity reconstruction relative to other algorithms on various specimens and over a range of signal-to-noise levels. Open-3DSIM also offers the capacity to extract dipole orientation, paving a new avenue for interpreting subcellular structures in six dimensions (xyzθλt). The platform is available as MATLAB code, a Fiji plugin and an Exe application to maximize user-friendliness.

Determination of optimal daily light integral (DLI) for indoor cultivation of iceberg lettuce in an indigenous vertical hydroponic system.

The indoor cultivation of lettuce in a vertical hydroponic system (VHS) under artificial lighting is an energy-intensive process incurring a high energy cost. This study determines the optimal daily light integral (DLI) as a function of photoperiod on the physiological, morphological, and nutritional parameters, as well as the resource use efficiency of iceberg lettuce (cv. Glendana) grown in an indoor VHS. Seedlings were grown in a photoperiod of 12 h, 16 h, and 20 h with a photosynthetic photon flux density (PPFD) of 200 µmol m-2 s-1 using white LED lights. The results obtained were compared with VHS without artificial lights inside the greenhouse. The DLI values for 12 h, 16 h, and 20 h were 8.64, 11.5, and 14.4 mol m-2 day-1, respectively. The shoot fresh weight at harvest increased from 275.5 to 393 g as the DLI increased from 8.64 to 11.5 mol m-2 day-1. DLI of 14.4 mol m-2 day-1 had a negative impact on fresh weight, dry weight, and leaf area. The transition from VHS without artificial lights to VHS with artificial lights resulted in a 60% increase in fresh weight. Significantly higher water use efficiency of 71 g FW/L and energy use efficiency of 206.31 g FW/kWh were observed under a DLI of 11.5 mol m-2 day-1. The study recommends an optimal DLI of 11.5 mol m-2 day-1 for iceberg lettuce grown in an indoor vertical hydroponic system.

Detection of Defective Lettuce Seedlings Grown in an Indoor Environment under Different Lighting Conditions Using Deep Learning Algorithms.

Sorting seedlings is laborious and requires attention to identify damage. Separating healthy seedlings from damaged or defective seedlings is a critical task in indoor farming systems. However, sorting seedlings manually can be challenging and time-consuming, particularly under complex lighting conditions. Different indoor lighting conditions can affect the visual appearance of the seedlings, making it difficult for human operators to accurately identify and sort the seedlings consistently. Therefore, the objective of this study was to develop a defective-lettuce-seedling-detection system under different indoor cultivation lighting systems using deep learning algorithms to automate the seedling sorting process. The seedling images were captured under different indoor lighting conditions, including white, blue, and red. The detection approach utilized and compared several deep learning algorithms, specifically CenterNet, YOLOv5, YOLOv7, and faster R-CNN to detect defective seedlings in indoor farming environments. The results demonstrated that the mean average precision (mAP) of YOLOv7 (97.2%) was the highest and could accurately detect defective lettuce seedlings compared to CenterNet (82.8%), YOLOv5 (96.5%), and faster R-CNN (88.6%). In terms of detection under different light variables, YOLOv7 also showed the highest detection rate under white and red/blue/white lighting. Overall, the detection of defective lettuce seedlings by YOLOv7 shows great potential for introducing automated seedling-sorting systems and classification under actual indoor farming conditions. Defective-seedling-detection can improve the efficiency of seedling-management operations in indoor farming.

Optimizing lettuce yields and quality by incorporating movable downward lighting with a supplemental adjustable sideward lighting system in a plant factory.

Background: Lettuce is a vegetable that is increasingly consumed globally, given its nutritional quality. Plant factories with artificial lighting can produce high-yield and high-quality plants. High plant density in these systems speeds up leaf senescence. Wasted energy and lower yield raised labor expenses are some of the bottlenecks associated with this farming system. In order to increase lettuce yields and quality in the plant factory, it is essential to develop cultivating techniques using artificial lighting. Methods: Romaine lettuce was grown under a developed "movable downward lighting combined with supplemental adjustable sideward lighting system" (C-S) and under a system without supplemental sideward lighting (N-S) in a plant factory. The effects of C-S on lettuce's photosynthetic characteristics, plant yield, and energy consumption relative to plants grown under a system without N-S were studied. Results: Romaine lettuce growth and light energy consumption in the plant factory were both influenced favorably by supplementary adjustable sideward lighting. The number of leaves, stem diameter, fresh and dry weights, chlorophyll a and b concentration, and biochemical content (soluble sugar and protein) all increased sharply. The energy consumption was substantially higher in the N-S treatment than the C-S.

Differential synthetic illumination based on multi-line detection for resolution and contrast enhancement of line confocal microscopy.

Line confocal (LC) microscopy is a fast 3D imaging technique, but its asymmetric detection slit limits resolution and optical sectioning. To address this, we propose the differential synthetic illumination (DSI) method based on multi-line detection to enhance the spatial resolution and optical sectioning capability of the LC system. The DSI method allows the imaging process to simultaneously accomplish on a single camera, which ensures the rapidity and stability of the imaging process. DSI-LC improves X- and Z-axis resolution by 1.28 and 1.26 times, respectively, and optical sectioning by 2.6 times compared to LC. Furthermore, the spatially resolved power and contrast are also demonstrated by imaging pollen, microtubule, and the fiber of the GFP fluorescence-labeled mouse brain. Finally, Video-rate imaging of zebrafish larval heart beating in a 665.6 × 332.8 µm2 field-of-view is achieved. DSI-LC provides a promising approach for 3D large-scale and functional imaging in vivo with improved resolution, contrast, and robustness.

MILO: Multi-Bounce Inverse Rendering for Indoor Scene With Light-Emitting Objects.

Recently, many advances in inverse rendering are achieved by high-dimensional lighting representations and differentiable rendering. However, multi-bounce lighting effects can hardly be handled correctly in scene editing using high-dimensional lighting representations, and light source model deviation and ambiguities exist in differentiable rendering methods. These problems limit the applications of inverse rendering. In this paper, we present a multi-bounce inverse rendering method based on Monte Carlo path tracing, to enable correct complex multi-bounce lighting effects rendering in scene editing. We propose a novel light source model that is more suitable for light source editing in indoor scenes, and design a specific neural network with corresponding disambiguation constraints to alleviate ambiguities during the inverse rendering. We evaluate our method on both synthetic and real indoor scenes through virtual object insertion, material editing, relighting tasks, and so on. The results demonstrate that our method achieves better photo-realistic quality.

Reflectional quantitative differential phase microscopy using polarized wavefront phase modulation.

Quantitative phase microscopy (QPM), as a label-free and nondestructive technique, has been playing an indispensable tool in biomedical imaging and industrial inspection. Herein, we introduce a reflectional quantitative differential phase microscopy (termed RQDPM) based on polarized wavefront phase modulation and partially coherent full-aperture illumination, which has high spatial resolution and spatio-temporal phase sensitivity and is applicable to opaque surfaces and turbid biological specimens. RQDPM does not require additional polarized devices and can be easily switched from reflectional mode to transmission mode. In addition, RQDPM inherits the characteristic of high axial resolution of differential interference contrast microscope, thereby providing topography for opaque surfaces. We experimentally demonstrate the reflectional phase imaging ability of RQDPM with several samples: semiconductor wafer, thick biological tissues, red blood cells, and Hela cells. Furthermore, we dynamically monitor the flow state of microspheres in a self-built microfluidic channel by using RQDPM converted into the transmission mode.

Motion-resistant structured illumination microscopy based on principal component analysis.

Structured illumination microscopy (SIM) has become one of the most significant super-resolution techniques in bioscience for observing live-cell dynamics, thanks to fast full-field imaging and low photodamage. However, artifact-free SIM super-resolution reconstruction requires precise knowledge about variable environment-sensitive illumination parameters. Conventional algorithms typically, under the premise of known and reliable constant phase shifts, compensate for residual parameters, which will be easily broken by motion factors such as environment and medium perturbations, and sample offsets. In this Letter, we propose a robust motion-resistant SIM algorithm based on principal component analysis (mrPCA-SIM), which can efficiently compensate for nonuniform pixel shifts and phase errors in each raw illumination image. Experiments demonstrate that mrPCA-SIM achieves more robust imaging quality in complex, unstable conditions compared with conventional methods, promising a more compatible and flexible imaging tool for live cells.

Large-field lattice structured illumination microscopy.

In this paper, we present large-field, five-step lattice structured illumination microscopy (Lattice SIM). This method utilizes a 2D grating for lattice projection and a spatial light modulator (SLM) for phase shifting. Five phase-shifted intensity images are recorded to reconstruct a super-resolution image, enhancing the imaging speed and reducing the photo-bleaching both by 17%, compared to conventional two-direction and three-shift SIM. Furthermore, lattice SIM has a three-fold spatial bandwidth product (SBP) enhancement compared to SLM/DMD-based SIM, of which the fringe number is limited by the SLM/DMD pixel number. We believe that the proposed technique will be further developed and widely applied in many fields.

Design and test of a laser lighting device for plant growth.

Aiming at the problems of high energy consumption, complex wiring, high layout cost, limited use environment, and limited function of conventional plant lighting equipment such as fluorescent lamps, sodium lamps, etc., this paper develops a type of laser device for plant growth with nanometer lasers based on the design of an intelligent control system of an immune algorithm, constant current driving circuit of the laser, pulse power supply, and rotatable intelligent platform to make the device more stable, reliable, practical, and energy efficient, and provides a useful reference for the innovation and application of materials, processes, and methods of plant lighting. The effects of nanometer laser light supplementation on the growth of purple lettuce, romaine lettuce, Chinese cabbage, and you-mai vegetable have been studied with the vegetables mentioned above as experimental materials and with natural light as the control sample. The results show that the nanometer laser device significantly increases stem height, stem thickness, leaf area, leaf number, and chlorophyll content, effectively promotes plant growth, and achieves efficient cultivation. In the future, studies of the effects of laser treatment on plant physiology and biochemistry will be sped up to explore the molecular biological mechanism of lasers to promote application and technological innovation of lasers in lighting for plant growth and the laser device in productivity.

TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light.

This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color temperature linking sensors. Analysis results for the measured natural light showed that irregular color temperature cycles were observed for more than 90% of the year due to the influence of regional weather and atmospheric conditions. Regular color temperature cycles were observed only on some clear days. The color temperature cycle dramatically affects the health of the occupants. However, since irregular color temperatures are difficult to predict and cannot easily generate cycles, only the color temperatures of some clear days are currently used, and the actual color temperature of natural light cannot be reproduced. There is little research on deriving real-time periodic characteristics and lighting services targeting irregular color temperatures of natural light. Therefore, this paper proposes a TadGAN (Time Series Anomaly Detection Using Generative Adversarial Networks)-based daily color temperature cycle generation method that responds to irregular changes in the natural light color temperature. A TadGAN model for generating the natural light color temperature cycle was built, and learning was performed based on the dataset extracted through the measured natural light characteristic Database. After that, the generator of TadGAN was repeatedly applied to generate a color temperature cycle close to the change of natural light. In the performance test of the proposed method, it was possible to generate periodic characteristics of the irregular natural light color temperature distribution.

NIR-II multifocal structured illumination microscopy.

Optical microscopy has been widely used as a versatile tool in biological research. However, its penetration depth and spatial resolution are desperately limited by light scattering during deep propagation in turbid medium. Here, we implement near-infrared second window (1000-1700 nm) multifocal structured illumination microscopy (NIR-II MSIM) capable of deep penetration, high contrast, and enhanced spatial resolution. Raster-scanning multifocal illumination patterns ensure homogeneous illumination of the sample. By integrating NIR-II photoemission into multifocal photoexcitation, NIR-II MSIM affords deep imaging with improved lateral resolution (∼1.49 µm) at a depth of 2.5 mm in an Intralipid/agar phantom and outstanding contrast. Additionally, imaging at longer wavelength in the NIR-II region shows superior performance. This NIR-II MSIM system will afford a promising platform for studying physiological phenomena in turbid specimens in the future.

Multi-scale fusion framework via retinex and transmittance optimization for underwater image enhancement.

Low contrast, poor color saturation, and turbidity are common phenomena of underwater sensing scene images obtained in highly turbid oceans. To address these problems, we propose an underwater image enhancement method by combining Retinex and transmittance optimized multi-scale fusion framework. Firstly, the grayscale of R, G, and B channels are quantized to enhance the image contrast. Secondly, we utilize the Retinex color constancy to eliminate the negative effects of scene illumination and color distortion. Next, a dual transmittance underwater imaging model is built to estimate the background light, backscattering, and direct component transmittance, resulting in defogged images through an inverse solution. Finally, the three input images and corresponding weight maps are fused in a multi-scale framework to achieve high-quality, sharpened results. According to the experimental results and image quality evaluation index, the method combined multiple advantageous algorithms and improved the visual effect of images efficiently.