Brightness Enhancement Research Articles

Side Chain Phenyl Isomerization-Induced Spatial Conjugation for Achieving Efficient NIR-II Phototheranostic Agents.

The contradiction of near-infrared II (NIR-II) emission and photothermal effects limits the development of phototheranostic agents (PTAs) in many emerging cutting-edge applications. Organic aggregates present a promising opportunity for the balance of competitive relaxation processes through the manipulation of molecular structure and packing. Herein, side chain phenyl isomerization-induced spatial conjugation was proposed for constructing A-D-A type NIR-II PTAs with simultaneous enhancement of fluorescence brightness and photothermal properties. Three pairs of mutually isomeric fluorophores, whose phenyls respectively located at the outside (o-series) and inside (i-series) of the side chain, were designed and synthesized. The positional isomerization of the phenyl endows the o-series crystals with strong spatial conjugation between the phenyl group on the side chain and the backbone, as well as interlocked planar network, which is different to that observed in the i-series. Thus, all o-series nanoparticles (NPs) exhibit red-shifted absorption, enhanced NIR-II emission, and superior photothermal properties than their i-series counterparts. A prominent member of the o-series, o-ITNP NPs, demonstrated efficacy in facilitating NIR-II angiography, tumor localization, and NIR-II imaging-guided tumor photothermal therapy. The success of this side chain phenyl isomerization strategy paves the way for precise control of the aggregation behavior and for further development of efficient NIR-II PTAs.

Side Chain Phenyl Isomerization‐Induced Spatial Conjugation for Achieving Efficient NIR‐II Phototheranostic Agents

The contradiction of near‐infrared II (NIR‐II) emission and photothermal effects limits the development of phototheranostic agents (PTAs) in many emerging cutting‐edge applications. Organic aggregates present a promising opportunity for the balance of competitive relaxation processes through the manipulation of molecular structure and packing. Herein, side chain phenyl isomerization‐induced spatial conjugation was proposed for constructing A‐D‐A type NIR‐II PTAs with simultaneous enhancement of fluorescence brightness and photothermal properties. Three pairs of mutually isomeric fluorophores, whose phenyls respectively located at the outside (o‐series) and inside (i‐series) of the side chain, were designed and synthesized. The positional isomerization of the phenyl endows the o‐series crystals with strong spatial conjugation between the phenyl group on the side chain and the backbone, as well as interlocked planar network, which is different to that observed in the i‐series. Thus, all o‐series nanoparticles (NPs) exhibit red‐shifted absorption, enhanced NIR‐II emission, and superior photothermal properties than their i‐series counterparts. A prominent member of the o‐series, o‐ITNP NPs, demonstrated efficacy in facilitating NIR‐II angiography, tumor localization, and NIR‐II imaging‐guided tumor photothermal therapy. The success of this side chain phenyl isomerization strategy paves the way for precise control of the aggregation behavior and for further development of efficient NIR‐II PTAs.

CT image segmentation of foxtail millet seeds based on semantic segmentation model VGG16-UNet

Foxtail millet is an important minor cereal crop rich in nutrients. Due to the small size of its seeds, there is little information on the diversity of its seed structure among germplasms, limiting the identification of genes controlling seed development and germination. This paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds, gaining insights into their internal features, distribution, and composition. A total of 100 foxtail millet varieties were scanned with X-ray computed tomography to obtain 3D reconstruction images and slices. Pre-processing steps were adopted to improve image segmentation accuracy, including noise reduction, rotation, contrast enhancement, and brightness enhancement. The experiment revealed that traditional OpenCV image processing methods failed to achieve precise segmentation, whereas deep learning models exhibited outstanding performance in segmenting seed CT slice images. We compared UNet, PSPNet, and DeepLabV3 models, selected different backbones and optimizers based on the dataset, and continuously adjusted learning rates and maximum training epochs to train the models. Results demonstrated that VGG16-UNet achieved an accuracy of 99.19% on the foxtail millet seed CT slice image dataset, outperforming PSPNet and DeepLabV3 models. Compared to ResNet-UNet, VGG16-UNet shows an improvement of approximately 3.18% in accuracy, demonstrating superior performance in accurately segmenting the inner glume, outer glume, embryo, and endosperm under various adhesion conditions. Accurate segmentation of foxtail millet CT images enables analysis of embryo size, endosperm size, and glume thickness, which impact germination, growth, and nutrition. This study fills a gap in small grain structure research, offering insights to optimize agriculture and molecular breeding for improved yield and quality.

Research on Soybean Seedling Stage Recognition Based on Swin Transformer

Accurate identification of the second and third compound leaf periods of soybean seedlings is a prerequisite to ensure that soybeans are chemically weeded after seedling at the optimal application period. Accurate identification of the soybean seedling period is susceptible to natural light and complex field background factors. A transfer learning-based Swin-T (Swin Transformer) network is proposed to recognize different stages of the soybean seedling stage. A drone was used to collect images of soybeans at the true leaf stage, the first compound leaf stage, the second compound leaf stage, and the third compound leaf stage, and data enhancement methods such as image rotation and brightness enhancement were used to expand the dataset, simulate the drone’s collection of images at different shooting angles and weather conditions, and enhance the adaptability of the model. The field environment and shooting equipment directly affect the quality of the captured images, and in order to test the anti-interference ability of different models, the Gaussian blur method was used to blur the images of the test set to different degrees. The Swin-T model was optimized by introducing transfer learning and combining hyperparameter combination experiments and optimizer selection experiments. The performance of the optimized Swin-T model was compared with the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models. The results show that the optimized Swin-T model has an average accuracy of 98.38% in the test set, which is an improvement of 11.25%, 12.62%, 10.75%, 1.00%, and 0.63% compared with the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models, respectively. The optimized Swin-T model is best in terms of recall and F1 score. In the performance degradation test of the motion blur level model, the maximum degradation accuracy, overall degradation index, and average degradation index of the optimized Swin-T model were 87.77%, 6.54%, and 2.18%, respectively. The maximum degradation accuracy was 7.02%, 7.48%, 10.15%, 3.56%, and 2.5% higher than the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models, respectively. In the performance degradation test of the Gaussian fuzzy level models, the maximum degradation accuracy, overall degradation index, and average degradation index of the optimized Swin-T model were 94.3%, 3.85%, and 1.285%, respectively. Compared with the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models, the maximum degradation accuracy was 12.13%, 15.98%, 16.7%, 2.2%, and 1.5% higher, respectively. Taking into account various degradation indicators, the Swin-T model can still maintain high recognition accuracy and demonstrate good anti-interference ability even when inputting blurry images caused by interference in shooting. It can meet the recognition of different growth stages of soybean seedlings in complex environments, providing a basis for post-seedling chemical weed control during the second and third compound leaf stages of soybeans.

Flaw detection of railway catenary insulator based on DP-YOLOv5 algorithm with bright and dark channel enhancement

Abstract To ensure the timely detection of safety hazards in overhead transmission lines with railroad conductors and improve the accuracy of night insulator defect detection, this paper proposes the DP-YOLOv5 algorithm with dark and light channel enhancement optimization. It improves the night insulator image quality by introducing the dark and light channel enhancement algorithm, builds a lightweight network by combining the DP-BS module, and adds the Shuffle Attention module to enhance the feature extraction and ensure detection accuracy. At the same time, the EC-Loss loss function is used to optimize the prediction frame adjustment, accelerate the model convergence, and improve detection efficiency and accuracy. The simulation results show that the insulator dataset processed by DP-YOLOv5 has an accuracy of 95.3%, a recall of 94.8%, an average accuracy of 95.5%, and FLOPs of 219.3. Compared with YOLOv5, the mapped value is improved by 0.9%, the F1 is improved by 1%, and the model parameter and FLOPs are reduced by 48.8% and 50.8%, respectively.

Research on the veneer defect image enhancement algorithm based on AMEF-AGC.

Aiming at the veneer defect image acquisition process is prone to the problems of blurred edges, inconspicuous contrast and distortion, which cannot show the defects clearly.To improve image analyzability and clarity, a veneer defect image enhancement method based on AMEF-AGC is proposed herein. First, a veneer defect image is subjected to Gamma correction to obtain multiple underexposed image sequences for which Gaussian and Laplacian pyramids are constructed to determine the weights of the multiple exposure sequence group images.Multiscale fusion is then performed based on these weights. Second, the fused image is converted into the HSV color space, where contrast and brightness enhancements are performed for the luminance component, and then converted back to the RGB color space to obtain an enhanced veneer defect image. Solid wood panels selected herein were pine, poplar and birch with defects including live knots, dead knots, and cracks.Compared with those obtained using several algorithms including AMEF, AGC, improved AGC and GC, this algorithm achieved 6.93% and 5.4% improvements in PSNR and SSIM metrics, respectively.Results demonstrated that the proposed method effectively enhanced veneer defect images with blurred and distorted edges, improved image clarity and visual quality, and made defect parts and details of the image clearer.

Halobacteria Formula Improvement of Skin Care-A Randomized, Double-Blind, Placebo-Controlled Clinical Study.

Halobacteria trueperi, an extremophilic microorganism thriving in high-salt environments, produces extracellular polysaccharides with potential anti-inflammatory and anti-aging properties. However, its clinical efficacy in skin improvement remains unclear. This study focuses on H. trueperi TCI66207, isolated from the Pacific Ocean at a depth of 662 m near Hualien, and its potential to enhance skin parameters, aiming to develop a novel functional formulation for pharmaceutical and cosmetic use. This sudy aims to evaluate the clinical efficacy of H. trueperi TCI66207 on various skin parameters and its potential for developing new functional cosmetic formulations. A total of 40 subjects were recruited and randomly divided into two groups: the test group applied a serum containing H. trueperi TCI66207, while the placebo group used a basic serum. Subjects were instructed to apply the serum twice daily for 4 weeks. Skin parameters, including moisture, brightness, elasticity, pigmentation (spots and UV spots), texture, wrinkles, pores, and collagen density, were assessed before and after the 4-week application period. After 4 weeks of using the H. trueperi TCI66207 serum, significant improvements were observed in all measured skin parameters compared to baseline, with notable enhancements in moisture, brightness, elasticity, texture, and collagen density, along with reductions in wrinkles, spots, and pore size. Halobacteria trueperi TCI66207 serum demonstrates a clear ability to improve skin conditions and delay signs of aging, making it a promising candidate for the development of new cosmetic formulations with potent anti-aging and skin-rejuvenating properties.

Synergistic deinking effect of neutral sodium sulfite with fungal hemicellulase enzyme for improved recycling of waste papers

The study examined the beneficial effects of the synergistic deinking and delignification process on the quality of recycled pulp. By utilizing a mixture of neutral sodium sulfite, sodium carbonate, and fungal hemicellulase enzyme, the research demonstrated that the paper achieved enhancements in brightness, whiteness, tensile and burst indices, while also exhibiting reduced yellowness and ERIC in comparison to paper derived from chemically deinked pulp. The high-resolution ESCA analysis demonstrated a reduction in lignin surface coverage, and enhancement in the O/C atomic ratios. FTIR spectra indicated notable alterations in the structure of fibers and the functional groups of deinked pulp fibers. XRD spectra confirmed a higher cellulose crystallinity index and FESEM micrographs illustrated a rougher surface with the emergence of new fibrils. Additionally, UV analysis of the pulp effluents indicated significant lignin absorbance, while post-bleaching with H2O2 further enhanced the reactivity of the pulp and the overall quality of the paper.

Mosaicking based optimal threshold image enhancement for violence detection with deep quadratic attention mechanism

Violence is a prevalent societal issue that poses significant threats to individuals and communities. To address this challenge, researchers have developed various machine learning models to detect and prevent violent incidents. The violence detection problem statement is a critical challenge that requires innovative and collaborative solutions. By leveraging advancements in artificial intelligence, machine learning, and data analytics, systems are developed to effectively detect violence, prevent its occurrence, and protect vulnerable individuals and communities. Therefore proposed framework utilizes Differential Evolution Algorithm (DEA) along with mosaicking process which helps in enhancing the quality of the image in order to detect the presence of violence. Hockey fight dataset consist of 40 video clips out of which 20 are labelled as fight videos and remaining 20 is labelled as non-fight videos. The image is split as 30:20 ratio and the image will undergo either brightness enhancement or contrast enhancement according to the threshold value obtained by the fitness function. In mosaicking process, Ruled KNN based SURF algorithm is employed in the proposed model as it works faster and helps in providing high accuracy which assist in detecting the violence present in the images. The classification technique employed in the proposed method is Deep Quadratic Attention Mechanism (DQAM). It helps in providing better results for image classification, reducing the convergence rate and helps in improving the normalization. The performance of the proposed method is evaluated using different performance metrics which includes PSNR, SSIM and proposed method is compared with previous phase model in comparative analysis for comparing the efficiency of the proposed and existing model.

Random Raman Lasing in Diode-Pumped Multi-Mode Graded-Index Fiber with Femtosecond Laser-Inscribed Random Refractive Index Structures of Various Shapes

Diode-pumped multi-mode graded-index (GRIN) fiber Raman lasers provide prominent brightness enhancement both in linear and half-open cavities with random distributed feedback via natural Rayleigh backscattering. Femtosecond laser-inscribed random refractive index structures allow for the sufficient reduction in the Raman threshold by means of Rayleigh backscattering signal enhancement by +50 + 66 dB relative to the intrinsic fiber level. At the same time, they offer an opportunity to generate Stokes beams with a shape close to fundamental transverse mode (LP01), as well as to select higher-order modes such as LP11 with a near-1D longitudinal random structure shifted off the fiber axis. Further development of the inscription technology includes the fabrication of 3D ring-shaped random structures using a spatial light modulator (SLM) in a 100/140 μm GRIN multi-mode fiber. This allows for the generation of a multi-mode diode-pumped GRIN fiber random Raman laser at 976 nm with a ring-shaped output beam at a relatively low pumping threshold (~160 W), demonstrated for the first time to our knowledge.

Synaptic Feature of Quantum Dot Light-Emitting Diodes for Visualization of Learning Process.

Brain-inspired electronics with synaptic functions hold significant promise for advancing artificial intelligent applications. In this study, we demonstrate the synaptic feature of quantum-dot light-emitting diodes (QLEDs), which can convert electrical pulses into synapse-like light signals (the brightness gradually increases as the electrical pulses are prolonged). These features are analogous to learning and forgetting in biological synapses. The enhancement of brightness can be attributed to the reduction of charge transfer from the quantum dots to ZnO electron transport layer and resistive switching effect. With an integrated complementary metal-oxide-semiconductor (CMOS) drive, arrayed synaptic QLEDs can simulate the visualization of brain-like learning processes, which can reduce the noise toward high image recognition rate (>95.0%) by deep neural networks. Our findings introduce a novel brain-inspired optoelectronic approach with potential applications in optical neuromorphic systems.

Low-light image enhancement guided by multi-domain features for detail and texture enhancement

Low-light image enhancement holds significant value in the fields of computer vision and image processing, such as in applications like surveillance photography and medical imaging. Images captured in low-light environments typically suffer from significant noise levels, low contrast, and color distortion. Although existing low-light image enhancement techniques can improve image brightness and contrast to some extent, they often introduce noise or result in over-enhancement, leading to the loss of detail and texture. This paper introduces an innovative approach to low-light image enhancement by fusing spatial and frequency domain features while optimizing them with multiple loss functions. The core of the algorithm lies in its multi-branch feature extraction, multi-loss function constraints, and a carefully designed model structure. In particular, the model employs an encoder-decoder architecture, where the encoder extracts spatial features from the image, the Fourier feature extraction module captures frequency domain information, and the histogram feature encoder-decoder module processes global brightness distribution. These extracted features are then fused and reconstructed in the decoder to produce the enhanced image. In terms of loss functions, the algorithm combines perceptual loss, structural similarity loss, Fourier loss, and histogram loss to ensure comprehensive and natural enhancement effects. The novelty of this algorithm lies not only in its multi-branch feature extraction design but also in its unique model structure, which synergistically improves image quality across different domains, effectively preventing over-enhancement, and ultimately achieving a balanced enhancement of brightness, details, and texture. Experimental results on multiple datasets, including SIDD, LOL, MIT-Adobe-FiveK, and LOL-v2-synthetic, demonstrate that the proposed method outperforms existing techniques in terms of image detail, texture, and brightness. Specifically, it achieves a PSNR of 27.52 dB on the LOL dataset, surpassing Wavelet Diffusion by 1.19 dB. Additionally, on the LOL-v2-synthetic dataset, it achieves a PSNR of 29.56 dB, exceeding Wavelet Diffusion by 3.06 dB. These results demonstrate a significant enhancement in the visual quality of low-light images.

Gold and Gold Alloys Electropolishing in Choline Chloride-Glycerol Deep Eutectic Solvents

Electropolishing (EP) is a finishing technique used to reduce the roughness of metallic surfaces while enhancing their brightness. This process enables the treatment of complex shaped parts and reduces processing time compared to conventional mechanical polishing methods. Electropolishing of precious metals typically requires the use of electrolytes containing cyanides and other toxic compounds, which reduces its attractiveness from an industrial perspective. Deep Eutectic Solvents (DES) are emerging as a good alternative for the chemical and electrochemical dissolution of valuable metals, like gold, silver and copper. However, their use in the specific context of electrochemical polishing of precious alloys has not been considered so far. This work aims to evaluate the effectiveness of choline chloride-based DES in electropolishing 18K gold alloys. Their performance will be assessed in terms of roughness reduction, brightness enhancement and color preservation, all characteristics associated with the presence of a uniform dissolution.

Detection of coal gangue based on MSRCR algorithm and improved lightweight YOLOv8n

ABSTRACT Traditional methods for coal gangue sorting exhibit low efficiency, significant safety hazards, and limited applicability. Existing machine vision-based coal gangue image recognition methods struggle to balance model recognition speed and accuracy. In response to these challenges, this paper first utilizes the improved MSRCR algorithm to process images, enhancing the dark areas of coal gangue images while ensuring uniform brightness enhancement and image clarity. Furthermore, a novel lightweight coal gangue recognition method is proposed based on YOLOv8n, aiming to reduce data redundancy and improve recognition accuracy. Experimental results demonstrate that the improved lightweight model has a computational load of 7.1 GFLOPs, representing only 86.6% of the original model. The model detection rate is 73 fps, a 17 fps improvement over the original model. The accuracy, recall rate, and average precision reach 98.1%, 97.6%, and 98.9%, respectively, with improvements of 1.1, 0.9, and 0.5% points over the original model. The missing detection phenomenon is avoided effectively, and the accuracy and portability of the model are improved.

Full-color, highly bright and stretchable electroluminescent device with Janus colors based on photoluminescent electrode for wireless dynamical display

Stretchable displays are essential for the upcoming era of flexible electronics as they are the foundation of interactive human–machine interface. To date, alternating current electroluminescent (ACEL) devices are regarded as one of the most practicable routes for stretchable displays but their colors are very limited for the demand of full-color displays. Herein, we developed an ACEL device with Janus and full color through photoluminescent electrodes. In the front of the devices, multiple colors are achieved, Red, Blue, Green, and Yellow, covering a 66 % National Television System Committee color gamut. In the back, mixed lights of White, Purple, and Cyan color are obtained, enabling devices with Janus colors. The brightness enhancement of highly desirable white light achieves 4.4-fold, which is also one of the best performances. Our strategy exploits the indispensable electrode for ACEL and avoids structural changes, endowing the devices with a much better stretchability and brightness as compared to traditional methods. Moreover, our strategy is a general method that is applicable to different stretchable ACEL devices. A wearable device of 8*8 pixels is assembled and can dynamically display information from smart phones through Bluetooth in real time, laying a considerable advancement towards stretchable displays with full color.

Ghost Reduction and Brightness Enhancement of Aerial Images Using Lens Diffusers in MMAP

Ghost Reduction and Brightness Enhancement of Aerial Images Using Lens Diffusers in MMAP

Retinex theory-based nonlinear luminance enhancement and denoising for low-light endoscopic images

BackgroundThe quality of low-light endoscopic images involves applications in medical disciplines such as physiology and anatomy for the identification and judgement of tissue structures. Due to the use of point light sources and the constraints of narrow physiological structures, medical endoscopic images display uneven brightness, low contrast, and a lack of texture information, presenting diagnostic challenges for physicians.MethodsIn this paper, a nonlinear brightness enhancement and denoising network based on Retinex theory is designed to improve the brightness and details of low-light endoscopic images. The nonlinear luminance enhancement module uses higher-order curvilinear functions to improve overall brightness; the dual-attention denoising module captures detailed features of anatomical structures; and the color loss function mitigates color distortion.ResultsExperimental results on the Endo4IE dataset demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The PSNR is 27.2202, SSIM is 0.8342, and the LPIPS is 0.1492. It provides a method to enhance image quality in clinical diagnosis and treatment.ConclusionsIt offers an efficient method to enhance images captured by endoscopes and offers valuable insights into intricate human physiological structures, which can effectively assist clinical diagnosis and treatment.

Low-illumination and noisy bridge crack image restoration by deep CNN denoiser and normalized flow module

When applying deep learning and image processing techniques for bridge crack detection, the obtained images in real-world scenarios have severe image degradation problem. This study focuses on restoring low-illumination bridge crack images corrupted by noise to improve the accuracy of subsequent crack detection and semantic segmentation. The proposed algorithm consists of a deep CNN denoiser and a normalized flow-based brightness enhancement module. By taking the noise spectrum as an input, the deep CNN denoiser restores image at a broad range of noise levels. The normalized flow module, employs a conditional encoder and a reversible network to map the distribution of normally exposed images to a Gaussian distribution, effectively improving the image brightness. Extensive experiments have demonstrated the approach can usefully recover low-illumination images corrupted by noise compared to the state-of-the-art methods. Furthermore, the algorithm presented in this study can also be applied to other image quality restoration with high generalization and robust abilities. And the semantic segmentation accuracy of the restored image is significantly improved.

Enhancing low-light images via dehazing principles: Essence and method

Given the visual resemblance between inverted low-light and hazy images, dehazing principles are borrowed to enhance low-light images. However, the essence of such methods remains unclear, and they are susceptible to over-enhancement. Regarding the above issues, in this letter, we present corresponding solutions. Specifically, we point out that the Haze Formation Model (HFM) used for image dehazing exhibits a Bidirectional Mapping Property (BMP), enabling adjustment of image brightness and contrast. Building upon this property, we give a comprehensive and in-depth theoretical explanation for why dehazing on inverted low-light image is a solution to the image brightness enhancement problem. Further, an Adaptive Full Dynamic Range Mapping (AFDRM) method is then proposed to guide HFM in restoring the visibility of low-light images without inversion, while overcoming the issue of over-enhancement. Extensive experiments validate our proof and demonstrate the efficacy of our method.

Mode management in Bottom‐Up, Parity‐Time‐Symmetric Micro‐Cavity Lasers

AbstractThe intrinsic non‐Hermiticity of photonic devices with tunable optical gain and loss makes them excellent platforms to explore and implement applications based on parity‐time symmetry, and a notable example is mode management in micro‐cavity lasers. Thus far, parity‐time‐symmetric lasers are fabricated via conventional top‐down etching processes, which are known to cause cavity sidewall roughness that is potentially detrimental to laser performance. Bottom‐up fabrication of parity‐time‐symmetric lasers, however, has seen limited success due to strict requirements on the uniformity of cavity morphology. Here, parity‐time‐symmetric lasing is demonstrated in coupled InP micro‐ring cavities grown directly by selective area epitaxy. With a facet engineering technique, ring laser cavities with a highly deterministic morphology are realized, enabling parity‐time‐symmetric laser designs. Furthermore, benefiting from the versatility and controllability of this bottom‐up process, lasing mode selectivity can be enhanced through coupling gap tuning and cavity shape engineering, leading to single‐mode lasing with a peak side mode suppression ratio exceeding 17 dB and a threefold single‐mode brightness enhancement compared to a single, uncoupled laser cavity. This work unlocks a lasing mode management strategy that is previously unavailable to bottom‐up laser cavities, which is a major step toward the realization of on‐chip, low‐loss, and single‐mode micro‐cavity lasers.