Bright Component Research Articles

Observational Study of Recurrent Jets Confined by Active Region Loops

With high spatial and temporal resolution data from the Solar Dynamics Observatory and the New Vacuum Solar Telescope (NVST), we present observations of recurrent jets confined by coronal loops that occurred in the active region NOAA 11726 from 02:00 to 12:00 UT on 2013 April 21. Three jets are clearly observed by the NVST in Hα line. These recurrent jets originate from the emerging bipolar magnetic region at the north of the active region. Half of them are related to the magnetic flux emergence, and the others are associated with the magnetic flux cancellation. Their velocities range from 80.6 ± 1.3 km s−1 to 433.6 ± 20.1 km s−1. Though they eject from the same source region, their shapes, sizes, and eruptive trajectories are not exactly the same. Most of them consist of cool (dark) and hot (bright) components. The differential emission measure distributions of the recurrent jets suggest that they are multithermal structures. The rotation directions of the recurrent jets are not consistent. Eight of them have a counterclockwise rotation, and the others have a clockwise rotation. The 12 recurrent jets are classified as blowout (accounting for 33%) and standard (accounting for 67%) jets. The velocity and density range of the blowout jets are slightly wider than those of the standard jets. The blowout jets have lower temperatures than the standard jets. These observational results suggest that the recurrent jets are probably triggered by recurrent magnetic reconnection between the emerging bipolar magnetic region and its overlying large-scale active region loops.

Two-dimensional anisotropic vortex–bright soliton and its dynamics in dipolar Bose–Einstein condensates in optical lattice

We study construction and dynamics of two-dimensional (2D) anisotropic vortex–bright (VB) soliton in spinor dipolar Bose–Einstein condensates confined in a 2D optical lattice (OL), with two localized components linearly mixed by the spin–orbit coupling and long-range dipole–dipole interaction (DDI). It is found that the OL and DDI can support stable anisotropic VB soliton in the present setting for arbitrarily small value of norm N. We then present a new method via examining the mean square error of norm share of bright component to implement stability analysis. It is revealed that one can control the stability of anisotropic VB soliton only by adjusting OL depth for a fixed DDI. In addition, the dynamics of the anisotropic VB soliton was studied by applying the kick to them. The mobility of the single kicked VB soliton is Rabbi-like oscillation. However, for the collision dynamics of two kicked anisotropic VB solitons, their properties mainly depend on their initial distance and OL, and they can realize the transition from the bright component to the vortex component. Our work may provide a convenient way to prepare and manipulate anisotropic VB soliton in high-dimensional space.

An Improved Method for Reconstruction and Enhancing Dark Images based on CLAHE

The obtained images are frequently flawed because of a variety of environmen- tal issues, particularly at night, such as inside illumination, cloudy weather, etc. The dark image has a compressed dynamic range that can be improved in order to see the finer details. This research effort proposes an improved lighting reflection model-based technique for improving extremely dim images. This improved method relies on reconstruction carried out via morphological processing with Top-hat transformation and Contrast Limited Adaptive His- togram Equalization (CLAHE). The HSV colour scheme is used to perceive the image, and the V component is estimated. The inverse of the intensity component (V) is calculated after normalising the intensity component. The negative image is then subjected to the CLAHE algorithm. The final step is to apply multiscale image enhancement to the obtained image. The brightness component of an image is adaptively adjusted via gamma enhancement. The outcomes of two gamma-enhanced photographs with various gamma values are produced after gamma enhancement. The significant information that can be used for image fusion is extracted from these images using principal com- ponent analysis. The weight value is adaptively determined during the PCA- based image fusion employing morphological Top-hat modification to enhance the image quality and highlight the erratic background pixels. The suggested technique emphasises edge and structural preservation while enhancing detail in extremely dim photos. Results from experimental validations demonstrate that the suggested strategy outperforms the current method in terms of both qualitative and quantitative measures.

Exciton Ground State Fine Structure and Excited States Landscape in Layered Halide Perovskites from Combined BSE Simulations and Symmetry Analysis

AbstractLayered halide perovskites are solution‐processed natural heterostructures where quantum and dielectric confinement down to the nanoscale strongly influence the optical properties, leading to stabilization of bound excitons. Detailed understanding of the exciton properties is crucial to boost the exploitation of these materials in energy conversion and light emission applications, with on‐going debate related to the energy order of the four components of the most stable exciton. To provide theoretical feedback and solve among contrasting literature reports, this work performs ab initio solution of the Bethe–Salpeter equation (BSE) for symmetrized reference Cs2PbX4 (X = I and Br) models, with detailed interpretation of the spectroscopic observables based on group‐theory analysis. Simulations predict the following Edark < Ein‐plane < Eout‐of‐plane fine‐structure assignment, consistent with recent magneto‐absorption experiments and obtain similar increase in dark/bright splitting when going from lead‐iodide to a lead‐bromide composition as found experimentally. The authors further suggest that polar distortions may lead to stabilization of the in‐plane component and end‐up in a bright lowest exciton component, discuss exciton landscape over a broad energy range and clarify the exciton spin‐character, when large spin‐orbit coupling is in play, to rationalize the potential of halide perovskites as triplet sensitizers in combination with organic dyes.

Adaptive Reflection Detection and Control Strategy of Pointer Meters Based on YOLOv5s

Reflective phenomena often occur in the detecting process of pointer meters by inspection robots in complex environments, which can cause the failure of pointer meter readings. In this paper, an improved k-means clustering method for adaptive detection of pointer meter reflective areas and a robot pose control strategy to remove reflective areas are proposed based on deep learning. It mainly includes three steps: (1) YOLOv5s (You Only Look Once v5-small) deep learning network is used for real-time detection of pointer meters. The detected reflective pointer meters are preprocessed by using a perspective transformation. Then, the detection results and deep learning algorithm are combined with the perspective transformation. (2) Based on YUV (luminance-bandwidth-chrominance) color spatial information of collected pointer meter images, the fitting curve of the brightness component histogram and its peak and valley information is obtained. Then, the k-means algorithm is improved based on this information to adaptively determine its optimal clustering number and its initial clustering center. In addition, the reflection detection of pointer meter images is carried out based on the improved k-means clustering algorithm. (3) The robot pose control strategy, including its moving direction and distance, can be determined to eliminate the reflective areas. Finally, an inspection robot detection platform is built for experimental study on the performance of the proposed detection method. Experimental results show that the proposed method not only has good detection accuracy that achieves 0.809 but also has the shortest detection time, which is only 0.6392 s compared with other methods available in the literature. The main contribution of this paper is to provide a theoretical and technical reference to avoid circumferential reflection for inspection robots. It can adaptively and accurately detect reflective areas of pointer meters and can quickly remove them by controlling the movement of inspection robots. The proposed detection method has the potential application to realize real-time reflection detection and recognition of pointer meters for inspection robots in complex environments.

A Cosmological Fireball with 16% Gamma-Ray Radiative Efficiency

Gamma-ray bursts (GRBs) are the most powerful explosions in the universe. How efficiently the jet converts its energy to radiation is a long-standing problem, which is poorly constrained. The standard model invokes a relativistic fireball with a bright photosphere emission component. A definitive diagnosis of GRB radiation components and the measurement of GRB radiative efficiency require prompt emission and afterglow data, with high resolution and wide band coverage in time and energy. Here, we present a comprehensive temporal and spectral analysis of the TeV-emitting bright GRB 190114C. Its fluence is one of the highest for all the GRBs that have been detected so far, which allows us to perform a high-resolution study of the prompt emission spectral properties and their temporal evolutions, down to a timescale of about 0.1 s. We observe that each of the initial pulses has a thermal component contributing ∼20% of the total energy and that the corresponding temperature and inferred Lorentz factor of the photosphere evolve following broken power-law shapes. From the observation of the nonthermal spectra and the light curve, the onset of the afterglow corresponding to the deceleration of the fireball is considered to start at ∼6 s. By incorporating the thermal and nonthermal observations, as well as the photosphere and synchrotron radiative mechanisms, we can directly derive the fireball energy budget with little dependence on hypothetical parameters, measuring a ∼16% radiative efficiency for this GRB. With the fireball energy budget derived, the afterglow microphysics parameters can also be constrained directly from the data.

Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification.

Specular Reflections often exist in the endoscopic image, which not only hurts many computer vision algorithms but also seriously interferes with the observation and judgment of the surgeon. The information behind the recovery specular reflection areas is a necessary pre-processing step in medical image analysis and application. The existing highlight detection method is usually only suitable for medium-brightness images. The existing highlight removal method is only applicable to images without large specular regions, when dealing with high-resolution medical images with complex texture information, not only does it have a poor recovery effect, but the algorithm operation efficiency is also low. To overcome these limitations, this paper proposes a specular reflection detection and removal method for endoscopic images based on brightness classification. It can effectively detect the specular regions in endoscopic images of different brightness and can improve the operating efficiency of the algorithm while restoring the texture structure information of the high-resolution image. In addition to achieving image brightness classification and enhancing the brightness component of low-brightness images, this method also includes two new steps: In the highlight detection phase, the adaptive threshold function that changes with the brightness of the image is used to detect absolute highlights. During the highlight recovery phase, the priority function of the exemplar-based image inpainting algorithm was modified to ensure reasonable and correct repairs. At the same time, local priority computing and adaptive local search strategies were used to improve algorithm efficiency and reduce error matching. The experimental results show that compared with the other state-of-the-art, our method shows better performance in terms of qualitative and quantitative evaluations, and the algorithm efficiency is greatly improved when processing high-resolution endoscopy images.

Single Image Dehazing Using Saturation Line Prior.

Saturation information in hazy images is conducive to effective haze removal, However, existing saturation-based dehazing methods just focus on the saturation value of each pixel itself, while the higher-level distribution characteristic between pixels regarding saturation remains to be harnessed. In this paper, we observe that the pixels, which share the same surface reflectance coefficient in the local patches of haze-free images, exhibit a linear relationship between their saturation component and the reciprocal of their brightness component in the corresponding hazy images normalized by atmospheric light. Furthermore, the intercept of the line described by this linear relationship on the saturation axis is exactly the saturation value of these pixels in the haze-free images. Using this characteristic of saturation, termed saturation line prior (SLP), the transmission estimation is translated into the construction of saturation lines. Accordingly, a new dehazing framework using SLP is proposed, which employs the intrinsic relevance between pixels to achieve a reliable saturation line construction for transmission estimation. This approach can recover the fine details and attain realistic colors from hazy scenes, resulting in a remarkable visibility improvement. Extensive experiments in real-world and synthetic hazy images show that the proposed method performs favorably against state-of-the-art dehazing methods. Code is available on https://github.com/LPengYang/Saturation-Line-Prior.

The Lockman-SpReSO project

Context. Extragalactic surveys are a key tool for better understanding the evolution of galaxies. Both deep and wide-field surveys serve to provide a clearer emerging picture of the physical processes that take place in and around galaxies, and to identify which of these processes are the most important in shaping the properties of galaxies. Aims. The Lockman Spectroscopic Redshift Survey using Osiris (Lockman-SpReSO) aims to provide one of the most complete optical spectroscopic follow-ups of the far-infrared (FIR) sources detected by the Herschel Space Observatory in the Lockman Hole (LH) field. The optical spectroscopic study of the FIR-selected galaxies supplies valuable information about the relation between fundamental FIR and optical parameters, including extinction, star formation rate, and gas metallicity. In this article, we introduce and provide an in-depth description of the Lockman-SpReSO project and of its early results. Methods. We selected FIR sources from Herschel observations of the central 24 arcmin ×24 arcmin of the LH field with an optical counterpart up to 24.5 RC(AB). The sample comprises 956 Herschel FIR sources, plus 188 additional interesting objects in the field. These are point X-ray sources, cataclysmic variable star candidates, high-velocity halo star candidates, radio sources, very red quasi-stellar objects, and optical counterparts of sub-millimetre galaxies. The faint component of the catalogue (RC(AB) ≥ 20) was observed using the OSIRIS instrument on the 10.4 m Gran Telescopio Canarias in multi-object spectroscopy (MOS) mode. The bright component was observed using two multi-fibre spectrographs: the AF2-WYFFOS at the William Herschel Telescope and the HYDRA instrument at the WYIN telescope. Results. From an input catalogue of 1144 sources, we measured a secure spectroscopic redshift in the range 0.03 ≲ z ≲ 4.96 for 357 sources with at least two identified spectral lines. In addition, for 99 sources that show only one emission or absorption line, a spectroscopic redshift was postulated based on the line and object properties, and photometric redshift. In both cases, properties of emission and absorption lines were measured. Furthermore, to characterize the sample in more depth with determined spectroscopic redshifts, spectral energy distribution (SED) fits were performed using the CIGALE software. The IR luminosity and the stellar mass estimations for the sample are also presented as a preliminary description.

Aesthetic principles of interpretation of early arias in the vocalist’s concert repertoire: air de cour

Aesthetic principles of interpretation of early arias in the vocalist’s concert repertoire: air de cour

Velocity-coherent substructure in TMC-1: inflow and fragmentation

ABSTRACT Filamentary structures have been found nearly ubiquitously in molecular clouds and yet their formation and evolution is still poorly understood. We examine a segment of Taurus Molecular Cloud 1 (TMC-1) that appears as a single, narrow filament in continuum emission from dust. We use the Regularized Optimization for Hyper-Spectral Analysis (ROHSA), a Gaussian decomposition algorithm that enforces spatial coherence when fitting multiple velocity components simultaneously over a data cube. We analyse HC5N (9–8) line emission as part of the Green Bank Ammonia Survey and identify three velocity-coherent components with ROHSA. The two brightest components extend the length of the filament, while the third component is fainter and clumpier. The brightest component has a prominent transverse velocity gradient of 2.7 ± 0.1 km s−1 pc−1 that we show to be indicative of gravitationally induced inflow. In the second component, we identify regularly spaced emission peaks along its length. We show that the local minima between pairs of adjacent HC5N peaks line up closely with submillimetre continuum emission peaks, which we argue is evidence for fragmentation along the spine of TMC-1. While coherent velocity components have been described as separate physical structures in other star-forming filaments, we argue that the two bright components identified in HC5N emission in TMC-1 are tracing two layers in one filament: a lower density outer layer whose material is flowing under gravity towards the higher density inner layer of the filament.

Enhancement and Restoration of Scratched Murals Based on Hyperspectral Imaging—A Case Study of Murals in the Baoguang Hall of Qutan Temple, Qinghai, China

Environmental changes and human activities have caused serious degradation of murals around the world. Scratches are one of the most common issues in these damaged murals. We propose a new method for virtually enhancing and removing scratches from murals; which can provide an auxiliary reference and support for actual restoration. First, principal component analysis (PCA) was performed on the hyperspectral data of a mural after reflectance correction, and high-pass filtering was performed on the selected first principal component image. Principal component fusion was used to replace the original first principal component with a high-pass filtered first principal component image, which was then inverse PCA transformed with the other original principal component images to obtain an enhanced hyperspectral image. The linear information in the mural was therefore enhanced, and the differences between the scratches and background improved. Second, the enhanced hyperspectral image of the mural was synthesized as a true colour image and converted to the HSV colour space. The light brightness component of the image was estimated using the multi-scale Gaussian function and corrected with a 2D gamma function, thus solving the problem of localised darkness in the murals. Finally, the enhanced mural images were applied as input to the triplet domain translation network pretrained model. The local branches in the translation network perform overall noise smoothing and colour recovery of the mural, while the partial nonlocal block is used to extract the information from the scratches. The mapping process was learned in the hidden space for virtual removal of the scratches. In addition, we added a Butterworth high-pass filter at the end of the network to generate the final restoration result of the mural with a clearer visual effect and richer high-frequency information. We verified and validated these methods for murals in the Baoguang Hall of Qutan Temple. The results show that the proposed method outperforms the restoration results of the total variation (TV) model, curvature-driven diffusion (CDD) model, and Criminisi algorithm. Moreover, the proposed combined method produces better recovery results and improves the visual richness, readability, and artistic expression of the murals compared with direct recovery using a triple domain translation network.

Radio source-component association for the LOFAR Two-metre Sky Survey with region-based convolutional neural networks

Context. Radio loud active galactic nuclei (RLAGNs) are often morphologically complex objects that can consist of multiple, spatially separated, components. Only when the spatially separated radio components are correctly grouped together can we start to look for the corresponding optical host galaxy and infer physical parameters such as the size and luminosity of the radio object. Existing radio detection software to group these spatially separated components together is either experimental or based on assumptions that do not hold for current generation surveys, such that, in practice, astronomers often rely on visual inspection to resolve radio component association. However, applying visual inspection to all the hundreds of thousands of well-resolved RLAGNs that appear in the images from the Low Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) at 144 MHz, is a daunting, time-consuming process, even with extensive manpower. Aims. Using a machine learning approach, we aim to automate the radio component association of large (>15 arcsec) radio components. Methods. We turned the association problem into a classification problem and trained an adapted Fast region-based convolutional neural network to mimic the expert annotations from the first LoTSS data release. We implemented a rotation data augmentation to reduce overfitting and simplify the component association by removing unresolved radio sources that are likely unrelated to the large and bright radio components that we consider using predictions from an existing gradient boosting classifier. Results. For large (>15 arcsec) and bright (>10 mJy) radio components in the LoTSS first data release, our model provides the same associations for 85.3% ± 0.6 of the cases as those derived when astronomers perform the association manually. When the association is done through public crowd-sourced efforts, a result similar to that of our model is attained. Conclusions. Our method is able to efficiently carry out manual radio-component association for huge radio surveys and can serve as a basis for either automated radio morphology classification or automated optical host identification. This opens up an avenue to study the completeness and reliability of samples of radio sources with extended, complex morphologies.

Chandra measurements of gas homogeneity and turbulence at intermediate radii in the Perseus Cluster

ABSTRACT We present a Chandra study of surface brightness fluctuations in the diffuse intracluster medium of the Perseus Cluster. Our study utilizes deep, archival imaging of the cluster core as well as a new mosaic of 29 short $\, 5\,$-ks observations extending in eight different directions out to radii of $r_{500} \sim 2.2\, r_{2500}$. Under the assumption that the distribution of densities at a given radius is log-normally distributed, two important quantities can be derived from the width of the log-normal density distribution on a given spatial scale: the density bias, that is equal to the square root of the clumping factor C; and the one-component turbulent velocity, vk,1D. We forward-model all contributions to the measured surface brightness, including astrophysical and particle background components, and account for the Poisson nature of the measured signal. Measuring the distribution of surface brightness fluctuations in 1 arcmin2 regions, spanning the radial range $0.3\rm {-}2.2\, r_{2500}$ ($7.8\rm {-}57.3\,$ arcmin), we find a small to moderate average density bias of around $3{{\ \rm per\ cent}}$ at radii below $1.6\, r_{2500}$. We also infer an average turbulent velocity at these radii of V1D < 400 km s−1. Direct confirmation of our results on turbulent velocities inferred from surface brightness fluctuations should be possible using the X-ray calorimeter spectrometers to be flown aboard the XRISM and Athena observatories.

Object Detection Using Deep Learning CenterNet Model with Multi-Head External Attention Mechanism

Deep learning algorithms are highly effective at handling complex and challenging tasks such as image classification and detection. Over the past few decades, there have been a variety of convolutional neural networks (CNNs) with varying architectures to improve accuracy in object detection. However, a variety of factors, including brightness, enclosures, viewing distances, and background components, can affect the appearance, size, and shape of objects making the object detection task even more challenging. A multi-head external attention mechanism-based CenterNet model has been proposed to enhance the accuracy in detection of objects. The feature extraction process is carried out using Hourglass-104 network and Adaptive Feature Pyramid Network (AFPN). High-level features are derived through contextual modeling using an adaptive feature fusion (AFF) module with multi-head external attention. A hybrid feature selection method called adaptive hybrid feature selection (AHFS) determines the best features followed by prediction of the objects by the improved CenterNet. In order to assess the object detection accuracy, the experiment was conducted using the MS-COCO dataset on average precision (AP), mean average precision (mAP), and average recall (AR) metrics. Our proffered method achieves 64.76% on the MS-COCO dataset, improving the accuracy by 2.5% compared to other state-of-the-art models.

Radio interferometric imaging of RS Oph bipolar ejecta for the 2021 nova outburst

The recurrent nova and symbiotic binary RS Oph erupted again in August 2021 for its eighth known outburst. We observed RS Oph 34 days after the outburst at 5 GHz with the European VLBI Network (EVN). The radio image is elongated over the east–west direction for a total extension of about 90 mas (or about 240 AU at the Gaia DR3 distance d = 2.68−0.15+0.17 kpc), and shows a bright and compact central component coincident with the Gaia astrometric position, and two lobes east and west of it, expanding perpendicular to the orbital plane. By comparing with the evolution of emission-line profiles on optical spectra, we found the leading edge of the lobes to be expanding at ∼7550 km s−1, and i = 54° as the orbital inclination of the binary. The 2021 radio structure is remarkably similar to that observed following the 2006 eruption. The obscuring role of the density enhancement on the orbital plane (DEOP) is discussed in connection to the time-dependent visibility of the receding lobe in the background to the DEOP, and the origin of the triple-peaked profiles is traced to the ring structure formed by the nova ejecta impacting the DEOP.

Robust Image Hashing Combining 3D Space Contour and Vector Angle Features

AbstractIn this paper, an image hashing scheme combining 3D space contour (TDSC) features with vector angle (VA) features is proposed. The proposed algorithm extracts the 3D contours of the local component variation features of the image and the expression changes of the local component of the image in the form of a 3D VA to improve the performance. First, the gray component of the color image is used to construct a 3D space and the contour change features of the local component of the gray image are extracted using multi-perspectives. Then, the opposite color component and the brightness component Y of the YCbCr color space are extracted from the input image. The angular features of several image components are, respectively, extracted in the 3D space. Finally, the TDSC features are combined with the VA features to obtain image hashing. The simulations demonstrate and validate that the proposed image hashing scheme not only has better classification performance compared with the other image hashing techniques but is also equipped with the performance of tamper localization.

Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model

To guarantee the safety of the power grid system, it is essential to proceed reliable powerline inspection. Insulators are key devices in the powerlines. Their major function is to achieve mechanical fixing and electrical insulation, they play a key role in power lines. Insulators are deployed outdoors. Therefore, ensuring the safe operation of insulators is significant in the powerline inspection. Among all the inspection method, visual inspection is the key way. However, problems such as large changes in outdoor lighting have a strong impact on the accuracy of insulator detection. To overcome the shortcomings of uneven illumination, low contrast and poor details display in outdoor images, in this paper we introduces an image enhancement method based on illumination correction and compensation. First, the input data is converted from RGB color space to the HSV space, and three components, H, S and V, are obtained. The saturation component S is enhanced adaptively, and the brightness component V is processed by multi-scale gradient domain guided filter (MGDGF). Then the illumination component of the image is extracted, and corrected by two-dimensional adaptive Gamma transformation. The new brightness component is fused by Retinex based models. It helps to enhance the dark details and overall brightness of the image. This method not only solves the uneven illumination problem of the image, but also improves the contrast and details, while maintaining the original naturalness. Further, we introduce a real-time one step detection model based on YOLOv5, to detect the defect of the insulator. We evaluate the proposed method on an open public dataset. The evaluation results demonstrate that our proposed method can get very competitive results while maintaining real-time performance.

Video adaptive watermark embedding and detection algorithm based on phase function equation

Abstract In order to improve the video watermark embedding strength and balance the transparency of the watermark system, this paper proposes a combination of all phase Biorthogonal Transform and watermark embedding to complete the experiment. Firstly, the all phase biorthogonal transform is described. Referring to the construction process of APBT, combined with APDF and DST, a new all phase biorthogonal transform, all phase discrete sinusoidal biorthogonal transform (APDSBT), is proposed. This paper makes full use of MPEG-2 compression format to embed watermark directly in DCT domain. The low-frequency coefficients in the DCT block of the brightness space of I frame are selected as the watermark embedding space. The brightness component of each image block is transformed by two-dimensional DCT in the unit of 8×8 image blocks. By introducing the idea of energy receiver into the detection of digital watermark, the following correlation detector can be obtained. Implemented with MATIAB and VC + +. In the experiment, the foreman video test sequence is used as the watermark carrier, and the copyright logo image designed by ourselves is used as the watermark image to test the video watermarking system. The results show that when the coding rate is 3Mvps, the accuracy of each plane is 100%, 98.48%, 98.12% and 96.27% respectively. When the coding rate is 2.6Mvps, the accuracy of each plane is 100%, 96.38%, 94.87% and 95.21% respectively. This algorithm selects the frame of video to embed watermark. On the premise of ensuring video quality, this algorithm is robust to common video watermark attacks (MPEG compression, frame loss, frame clipping and frame rearrangement).

Research on low illumination coal gangue image enhancement based on improved Retinex algorithm

ABSTRACT Aiming at the problems of coal and gangue image recognition in low illumination or high dust concentration environment, an adaptive enhancement method based on improved Retinex algorithm, is proposed: H-GF-MSR algorithm. The algorithm is processed in HSV color space, uses guided filter for multi-scale Retinex algorithm in brightness component, and carries out adaptive saturation stretching in saturation component. Then the images are processed by histogram equalization, and the two algorithms are fused to improve the accuracy. Compared with SSR algorithm, MSRCR algorithm and other classical algorithms, this algorithm is improved in suppressing the generation of halo and image edge enhancement. The experimental results show that in terms of image evaluation indexes such as information entropy, image mean, image average gradient and peak signal-to-noise ratio, compared with SSR algorithm, the algorithm improves by 5.4%, 41.1%, 33.2% and 0.4% respectively; compared with MSRCR algorithm, the average improvement is 2.9%, 32.4%, 31.7% and 0.7% respectively. Through the enhancement experiments of coal gangue image in the illumination range of 10 lux ~ 200 lux, the algorithm has better enhancement effect. It can effectively suppress the bright light and restore the original details of the object.