Visibility Enhancement Research Articles

Reductively-induced carbon vacancies facilitate visible light-driven hydrogen evolution enhancement of g-C3N4

Graphite nitrogen carbide (g-C3N4, GCN) has attracted extensive attention. Herein, the g-C3N4 precursor was obtained through a conventional pyrolysis condensation path employing melamine as raw material. The g-C3N4 with abundant C vacancies was prepared with sodium borohydride (NaBH4) as reductant in different concentrations. Under visible light irradiation, the 3NBCN catalyst treated with 3 mol L−1 NaBH4 solution reveals outstanding photocatalytic hydrogen production activity, reaching 1764.9 μmol g−1 h−1, which is 7.56 folds higher than that of the reference GCN (206.3 μmol g−1 h−1). The 3NBCN also exhibits remarkable stability in the photocatalytic cyclic hydrogen production experiment. As a control experiment, Cr(VI) reduction further attests that g-C3N4 reduced by NaBH4 has an increased photocatalytic performance. The reduction rate of Cr(VI) over 3NBCN is 3.38 folds higher than that over the reference GCN. Multiple characterization results and density functional theory (DFT) calculations prove that the boosted photocatalytic activity benefits from the successful introduction of C defects, which promotes that photoexcited charges separate and transfer considerably faster and more efficiently. Considering all our results, a hypothesis for the mechanism of enhancement in photocatalytic performance is presented.

High-contrast interaction-free quantum imaging method

Quantum imaging techniques offer enhanced resolution, contrast, and precision at ultralow illumination levels compared to traditional imaging approaches. Relying on the unique properties of entangled photon pairs, two of these techniques stand out: the correlation-based quantum imaging technique provides visibility enhancement in imaging of a low-reflectivity object which is subject to excessive noise and losses, while the interaction-free ghost imaging allows for probing the presence of an object with an ultimately low number of photons. Here we propose a quantum imaging scheme that combines the unique advantages of these two approaches. We show that this scheme offers high-contrast imaging of objects with a minimal number of photons that can minimize thermal noise efficiently and create background-free images. We anticipate that this approach can find application in the imaging of photosensitive biological tissues in a noninvasive and harm-free fashion.

Exploring visible light enhancement for sensing: an azo-dye decorated gold nanoantenna monitored with a smartphone app.

Optical sensors can be used to detect a variety of substances ranging from diagnostics on biological samples to the detection of hazardous substances. This type of sensor can be a valuable alternative to more complex analytical techniques, being fast and requiring little to no sample preparation at the expense of the reusability of the device. Here, we show the construction of a colorimetric nanoantenna sensor using gold nanoparticles (AuNPs) embedded in poly(vinyl alcohol) (PVA) and decorated with the methyl orange (MO) azo dye (AuNP@PVA@MO) that is potentially reusable. As a proof of concept, we apply this sensor to detect H2O2 both visually and using a smartphone-based app for colorimetric measurements. Furthermore, through chemometric modeling of the app data, we can reach a detection limit of 0.0058% (1.70 mmolL-1) of H2O2 while being able to visually detect changes on the sensor. Our results reinforce the combination of nanoantenna sensors with chemometric tools as guidelines for sensor design. Finally, this approach can lead to novel sensors allowing for the visual detection of analytes in complex samples and their quantification using colorimetry.

Fast image visibility enhancement based on active polarization and color constancy for operation in turbid water.

Vehicles operating in a water medium sometimes encounter harsh conditions with high turbidity and low scene illumination, making it challenging to obtain reliable target information through optical devices. Although many post-processing solutions were proposed, they are not applicable to continuous vehicle operations. Inspired by the advanced polarimetric hardware technology, a joint fast algorithm was developed in this study to address the above problems. Backscatter attenuation and direct signal attenuation were solved separately by utilizing the revised underwater polarimetric image formation model. A fast local adaptive Wiener filtering method was used to improve the backscatter estimation by reducing the additive noise. Further, the image was recovered using the fast local space average color method. By using a low-pass filter guided by the color constancy theory, the problems of nonuniform illumination caused by artificial light and direct signal attenuation were both addressed. The results of testing on images from laboratory experiments showed improved visibility and realistic chromatic rendition.

The visible light photocatalytic H2 evolution and degradation enhancement of g-C3N4/Er:CdSe/SiO2 ternary core-shell heterojunction with up-conversion fluorescence auxiliary

The g-C3N4/Er:CdSe/SiO2 ternary core-shell heterojunction with up-conversion fluorescence auxiliary is fabricated via a simple chemical-calcination method. As revealed, the g-C3N4/Er:CdSe/SiO2 ternary core-shell heterojunction (g-C3N4/Er:CdSe/SiO2-3) exhibits remarkable visible light photocatalytic hydrogen evolution and photodegradation tetracycline hydrochloride enhancement than that of single CdSe (∼35 folds of HER, ∼2 folds of photodegradation) and single g-C3N4 (∼70 folds of HER, ∼2 folds of photodegradation), and achieves decent photocatalytic stability. It can be mainly attributed to that, the SiO2 spheres act as optical lens and Er-doping with up-conversion fluorescence can increase solar efficiency, and the formed g-C3N4/Er:CdSe heterojunction with appropriate potential gradient can improve carrier efficiency, including increasing transportation, prolonging lifetime and decreasing recombination. What’s more, the ternary core-shell spheroidal structure can increase active sites and photocatalytic stability.

Deep learning-driven surveillance quality enhancement for maritime management promotion under low-visibility weathers

Visual sensors are widely employed for real-time maritime surveillance. But they always suffer from some low-visibility problems, typically low light and haze, which greatly reduce the effectiveness of maritime management. In practical applications, the most common strategy is using separate networks to overcome different low-visibility problems. It needs large space for parameter storage. Besides, when the weather changes, the enhancement methods need to be toggled artificially, and the enhancement effect is unsatisfactory due to the lack of maritime dataset. To tackle these problems, we designed a learned parameter sharing (LPS)-based versatile visibility enhancement network (LPSNet) and trained it on the synthetic maritime dataset. It enhances the quality of captured surveillance data under both low-light and hazy environments via a versatile model. Due to the LPS between different low-visibility enhancement tasks, the proposed network performs better on the mutual problems, like noise suppression and detail preservation. Therefore, our LPSNet achieves superior enhancement effect on both low-light enhancement and dehazing. Besides, the running time is shorter than most of the previous methods, which can enhance 800 × 600 images over 18 FPS. Moreover, the comparison experiment on vessel detection task indicates the benefits of the proposed method on maritime management promotion under low-visibility weathers. The source code is available at .

Visible property enhancement techniques of IoT cameras using machine learning techniques

Visible property enhancement techniques of IoT cameras using machine learning techniques

Visible property enhancement techniques of IoT cameras using machine learning techniques

Visible property enhancement techniques of IoT cameras using machine learning techniques

Evaluation of blood-brain barrier integrity by the analysis of dynamic contrast-enhanced MRI - a comparison of quantitative and semi-quantitative methods.

Disruption of the blood-brain barrier (BBB) is a key feature of various brain disorders. To assess its integrity a parametrization of dynamic magnetic resonance imaging (DCE MRI) with a contrast agent (CA) is broadly used. Parametrization can be done quantitatively or semi-quantitatively. Quantitative methods directly describe BBB permeability but exhibit several drawbacks such as high computation demands, reproducibility issues, or low robustness. Semi-quantitative methods are fast to compute, simply mathematically described, and robust, however, they do not describe the status of BBB directly but only as a variation of CA concentration in measured tissue. Our goal was to elucidate differences between five semi-quantitative parameters: maximal intensity (Imax), normalized permeability index (NPI), and difference in DCE values between three timepoints: baseline, 5 min, and 15 min (delta5-0, delta15-0, delta15-5) and two quantitative parameters: transfer constant (Ktrans) and an extravascular fraction (Ve). For the purpose of comparison, we analyzed DCE data of four patients 12-15 days after the stroke with visible CA enhancement. Calculated parameters showed abnormalities spatially corresponding with the ischemic lesion, however, findings in individual parameters morphometrically differed. Ktrans and Ve were highly correlated. Delta5-0 and delta15-0 were prominent in regions with rapid CA enhancement and highly correlated with Ktrans. Abnormalities in delta15-5 and NPI were more homogenous with less variable values, smoother borders, and less detail than Ktrans. Moreover, only delta15-5 and NPI were able to distinguish vessels from extravascular space. Our comparison provides important knowledge for understanding and interpreting parameters derived from DCE MRI by both quantitative and semi-quantitative methods.

Underwater polarization imaging for visibility enhancement of moving targets in turbid environments.

Polarization imaging techniques have more prominent advantages for imaging in strongly scattered media. Previous de-scattering methods of polarization imaging usually require the priori information of the background region, and rarely consider the effect of non-uniformity of the optical field on image recovery, which not only reduces the processing speed of imaging but also introduces errors in image recovery, especially for moving targets in complex scattering environments. In this paper, we propose a turbid underwater moving image recovery method based on the global estimation of the intensity and the degree of polarization (DOP) of the backscattered light, combined with polarization-relation histogram processing techniques. The full spatial distribution of the intensity and the DOP of the backscattered light are obtained by using frequency domain analysis and filtering. Besides, a threshold factor is set in the frequency domain low-pass filter, which is used to adjust the execution region of the filter, which effectively reduces the error in image recovery caused by estimating the DOP of the backscattered light as a constant in traditional methods with non-uniform illumination. Meanwhile, our method requires no human-computer interaction, which effectively solves the drawbacks that the moving target is difficult to be recovered by traditional methods. Experimental studies were conducted on static and moving targets under turbid water, and satisfactory image recovery quality is achieved.

A deep thermal-guided approach for effective low-light visible image enhancement

Low-light visible image enhancement is important for various visual computing applications under conditions of poor lighting or hazardous weather. However, existing low-light image enhancement methods are mostly based on a single visible channel and cannot achieve satisfactory performance when processing real-captured nighttime images. In this paper, we attempt to utilize the complementary edge/texture features presented in thermal images to provide a stable guidance map to facilitate the enhancement of features extracted on low-light visible images. For this purpose, we propose a novel Central Difference Convolution-based Multi-Receptive-Field (CDC-MRF) module to effectively extract multi-scale edge/texture features on thermal images. Then, we design a thermal-guided convolutional block (TGCB) to enhance the low-light visible features under the guidance of thermal features. To our best knowledge, the proposed thermal-guided low-light image enhancement network (TGLLE-Net) represents the first attempt to perform low-light visible image enhancement by incorporating complementary information presented in both visible and thermal channels. The advantages of the proposed TGLLE-Net are twofold. Firstly, it is capable of suppressing severe noise disturbance presented in low-light visible images under the guidance of low-frequency components in thermal images. Moreover, TGLLE-Net can promote detail/appearance restoration of objects with distinctive thermal features (e.g., pedestrians, vehicles, and buildings). Both objective and subjective evaluation results demonstrate that our proposed TGLLE-Net outperforms state-of-the-art methods in terms of restoration accuracy, visual perception, and computational efficiency.

Efficient heterostructure of CuS@BiOBr for pollutants removal with visible light assistance

The Z-scheme heterojunction photocatalyst CuS@BiOBr was fabricated by precipitating BiOBr on CuS nanoparticles. The experimental results showed that CuS@BiOBr system showed high efficiency in the photocatalytic degradation of ciprofloxacin (CIP) compared with BiOBr and CuS. Catalyst application in realistic wastewater: CIP of removal ratio was 86.49%, 76.95%, 78.58% and 75.11% in DI water, tap water, lake water (longitude123.701077, latitude 41.453335) as well as river water (longitude 123.701710, latitude 41.453070), separately. These results show that CuS@BiOBr-700 can be effectively used for the photocatalytic removal of ciprofloxacin contaminants in the environment. • Composite catalyst of CuS@BiOBr was synthesized through a two-step hydrothermal method. • The CuS@BiOBr exhibited enhanced activity for CIP oxidation under visible-light. • Visible light response enhancement effect of CuS was demonstrated. • Composite catalyst of CuS@BiOBr has superior durability. Constructing the Z-scheme photocatalyst has been seen as an effective strategy to facilitate the removal of antibiotic contamination by photocatalysts. We constructed the Z-type heterojunction photocatalyst CuS@BiOBr by precipitating BiOBr on CuS nanoparticles. The experimental results showed that the CuS@BiOBr in comparison to BiOBr and CuS system exhibited highly efficient photocatalytic degradation of ciprofloxacin (CIP). The degradation rate of CIP under CuS, BiOBr and CuS@BiOBr-700 heterojunction were 19.28%, 53.26% and 86.49%, respectively. The structure, morphology, porosity, optical performance and photoelectrochemical properties of CuS@BiOBr were characterized by multifarious characterization methods. The influence of environmental factors (different water sources and initial pH) on the degradation of ciprofloxacin (CIP) was investigated. A Z-scheme charge transfer mechanism was confirmed by free radical trapping experiments, which demonstrated that the photoexcited holes (h + ) and superoxide radicals (⋅O 2 ¯ ) were the major active species.

Chitosan-based carbon dots with multi-color-emissive tunable fluorescence and visible light catalytic enhancement properties

Chitosan-based carbon dots with multi-color-emissive tunable fluorescence and visible light catalytic enhancement properties

Visibility enhancement of underwater images based on polarization common-mode rejection of a highly polarized target signal.

Underwater active polarization imaging is promising due to its effect of significantly descattering. Polarization-difference is commonly used to filter out backscattered noise. However, the polarization common-mode rejection of target signal has rarely been utilized. In this paper, via taking full advantage of this feature of Stokes vectors S2 which ably avoids interference from target light, the spatial variation of the degree of polarization of backscattered light is accurately estimated, and the whole scene intensity distribution of background is reconstructed by Gaussian surface fitting based on least square. Meanwhile, the underwater image quality measure is applied as optimization feedback, through iterative computations, not only sufficiently suppresses backscattered noise but also better highlights the details of the target. Experimental results demonstrate the effectiveness of the proposed method for highly polarized target in strongly scattering water.

Nanotechnology for enhancement of sonographic needle visibility

Category: Interventional radiology and vascular

MdedFusion: A multi-level detail enhancement decomposition method for infrared and visible image fusion

To improve the infrared target features and preserve the texture details from source images, a simple and efficient multi-level detail enhancement decomposition method based on weighted least squares (WLS) and local statistical edge model (LSEM) is proposed, termed as MdedFusion. First, visibility enhancement of visible image is performed by using guided filter to avoid the effects of low-light environments. Then, infrared image and enhanced visible image are decomposed into a series of smooth parts and detail parts by WLS filter based multi-level decomposition mechanism. Next, in order to highlight edge features, a novel fusion strategy based on LSEM is proposed to make full use of variance difference for fusion of detail parts, and smooth parts are simply fused by weighted average (WAVG) strategy which is an efficient operation in our fusion framework. Finally, the fused image is obtained by reconstructing the fused smooth part and detail part. Specially, particle swarm optimization (PSO) algorithm is introduced to adaptively optimize the filter parameters in our framework. Compared with 16 state-of-the-art fusion methods, the experimental results show that the superiority of our proposed MdedFusion surpass the compared methods in highlighting infrared features and preserving texture detail information.

Visible light emission enhancement from a graphene-based metal Fabry-Pérot cavity.

The high saturation current density and ultrafast heating modulation of graphene makes it a competitive candidate for future thermal emission source. However, the low emissivity and easy oxidation under high temperature in air limit graphene application in the spectral range from the visible to near infrared. Here, we report a visible graphene thermal emitter based on the metal Fabry-Pérot (FP) cavity, which can greatly enhance the emissivity of graphene at wavelength around 637 nm and protect graphene from oxidation. We investigate the temperature characteristics of the emitter, and find the temperature of hot electrons in graphene is much higher than that of graphene lattice. Moreover, we also demonstrate the wavelength and intensity of graphene emission could be controlled by tuning the dielectric thickness between two gold layers. These results are helpful in the development of advanced graphene electro-thermal emission controlling application.

Multispectral photoacoustic fluctuation imaging for full visibility SO2 imaging

Photoacoustic (PA) imaging (PAI) images blood vessels through the hemoglobin contrast. However, conventional images are affected by visibility artefacts which prevents seeing all the blood vessels morphology. We introduced PA fluctuation imaging (PAFI) exploiting the absorption fluctuations due to blood flow. Here, we demonstrate how PAFI enhances the image quality and elaborate if it can be used for quantitative multispectral (MS) imaging for 3D blood oxygenation (SO2) imaging. A spherical array (256 elements, 8 MHz, Imasonic) connected to a Verasonics Vantage scanner, was coupled to the laser (Innolas Spitlight) through a fiber bundle (Ceramoptek). The PAFI sequence consisted in acquiring 1000 frames at 100 Hz repetition rate while scanning continuously the wavelength from 680 nm to 880 nm. Image processing resulted in PAFI image per wavelength. The compensation of predictable effects of pertubations provided quantitative SO2 images with visibility and contrast enhancement. The technique was validated in glass capillaries and in the chicken embryo model by comparing the SO2 values with the one obtained in the visible structures using conventional imaging (resp. 3.5% and 9% errors). Thus, MS-PAFI can provide quantitative SO2 full-visibility 3D imaging with an intrinsic specificity to blood which simplifies spectral unmixing.

Sub-pixel energy-weighting techniques for metallic contaminant highlighting in a pharmaceutical hard capsule using a Timepix3 CdZnTe hybrid pixel detector

The presented work describes the capabilities of a state-of-the-art spectroscopic photon-counting hybrid pixel detector for use in non-destructive testing, as a contaminant detector with potential uses in the food and pharmaceutical industries. A pharmaceutical hard capsule containing vitamin powder, contaminated with Steel (ST) and Tungsten (W) was prepared under controlled conditions. Both contaminants are distinguished from the powder by combining sub-pixel imaging with spectroscopic imaging analysis, made possible by the use of a 2 mm CdZnTe sensor coupled to a Timepix3 ASIC, operated in Time-over-Threshold and Time-of-Arrival mode. The sub-pixelization technique achieves a spatial resolution of 18.3 μm, with a maximum photon flux of 2.5× 106 photons/s· cm2. For the spectral analysis technique implemented, each energy bin image is energy-weighted, with weights optimally defined by considering the bin contrast and noise behavior, and combined to form an integrated-spectrum enhanced image with increased contaminant visibility. Contrast-to-Noise Ratio (CNR) for each contaminant type was used for performance assessment of the approach. A CNR increase of 106% above the reference unweighted-spectrum image was achieved for the ST contaminant, while the denser W contaminant showed a CNR increase of 80%. This means that the presented spectroscopic imaging technique is feasible for implementation in material visibility enhancement.

Infrared and low-light visible image fusion based on hybrid multiscale decomposition and adaptive light adjustment

Most traditional infrared and visible image fusion methods often ignore weak texture details, especially for the low-light visible images in which the weak details are easily drowned due to noise or ill-illumination. To address this problem, we propose a novel infrared and low-light visible image fusion method from the perspective of low-light visible image enhancement, weak feature extraction strategy and detail preserved fusion rules. By combining both local and global contrast enhancements, an adaptive light adjustment algorithm is proposed to improve the brightness and texture details of low-light visible images. In addition, we design a hybrid multiscale decomposition model based on guided filters (GFs) and side window guided filters (SWGFs) to decompose the source images into the base layer, large-scale detail layers and small-scale detail layers. The three layers reflects the background, large edge structures and weak texture details of source images respectively. Subsequently, the visual saliency retention, normalized arctan function, and edge preservation-based consistency verification are applied to highlight salient targets and retain weak details for three layers fusion. Qualitative and quantitative experimental results on publicly available datasets prove the superiority of our method over state-of-the-art methods in terms of highlight salient targets, avoiding edge blurring, and retaining weak details.