Local Brightness Research Articles

A new method for estimating nanoparticle deposition coverage from a set of weak-contrast SEM images

Imaging nanomaterials in hybrid systems is critical to understanding the structure and functionality of these systems. However, current technologies such as scanning electron microscopy (SEM) may obtain high resolution/contrast images at the cost of damaging or contaminating the sample. For example, to prevent the charging of organic substrate/matrix, a very thin layer of metal is coated on the surface, which will permanently contaminate the sample and eliminate the possibility of reusing it for following processes. Conversely, examining the sample without any modifications, in pursuit of high-fidelity digital images of its unperturbed state, can come at the cost of low-quality images that are challenging to process. Here, a solution is proposed for the case where no brightness threshold is available to reliably judge whether a region is covered with nanomaterials. The method examines local brightness variability to detect nanomaterial deposits. Very good agreement with manually obtained values of the coverage is observed, and a strong case is made for the method's automatability. Although the developed methodology is showcased in the context of SEM images of Polydimethylsiloxane (PDMS) substrates on which silicone dioxide (SiO2) nanoparticles are assembled, the underlying concepts may be extended to situations where straightforward brightness thresholding is not viable.

Enhanced low-light image fusion through multi-stage processing with Bayesian analysis and quadratic contrast function

This manuscript introduces an innovative multi-stage image fusion framework that adeptly integrates infrared (IR) and visible (VIS) spectrum images to surmount the difficulties posed by low-light settings. The approach commences with an initial preprocessing stage, utilizing an Efficient Guided Image Filter for the infrared (IR) images to amplify edge boundaries and a function for the visible (VIS) images to boost local contrast and brightness. Utilizing a two-scale decomposition technique that incorporates Lipschitz constraints-based smoothing, the images are effectively divided into distinct base and detail layers, thereby guaranteeing the preservation of essential structural information. The process of fusion is carried out in two distinct stages: firstly, a method grounded in Bayesian theory is employed to effectively combine the base layers, so effectively addressing any inherent uncertainty. Secondly, a Surface from Shade (SfS) method is utilized to ensure the preservation of the scene's geometry by enforcing integrability on the detail layers. Ultimately a Choose Max principle is employed to determine the most prominent textural characteristics, resulting in the amalgamation of the base and detail layers to generate an image that exhibits a substantial enhancement in both clarity and detail. The efficacy of our strategy is substantiated by rigorous testing, showcasing notable progressions in edge preservation, detail enhancement, and noise reduction. Consequently, our method presents significant advantages for real-world applications in image analysis.

НЕЧЕТКИЙ МЕТОД ПОВЫШЕНИЯ КОНТРАСТНОСТИ КАРТОГРАФИЧЕСКИХ ИЗОБРАЖЕНИЙ НИЗКОГО КАЧЕСТВА

In this study, we have developed a new image enhancement method that uses fuzzy logic. This method allows us to split pixel values into different levels of importance, which helps to compensate for the loss of local brightness in dark and bright areas of an image. The goal is to increase the overall brightness of the image while preserving details. The process involves three stages. Firstly, the satellite image is transformed into a membership space using the c-means clustering algorithm. This creates a model that can be used to convert each pixel value into a level of importance. Secondly, a corresponding model is created for each level of importance based on the membership data. Finally, the image is transformed back into a standard brightness space by combining the grayscale values for each level. Our results show that this method improves the visual quality and accuracy of measurements when compared to traditional methods.

Enhancing robustness in asynchronous feature tracking for event cameras through fusing frame steams

Event cameras produce asynchronous discrete outputs due to the independent response of camera pixels to changes in brightness. The asynchronous and discrete nature of event data facilitate the tracking of prolonged feature trajectories. Nonetheless, this necessitates the adaptation of feature tracking techniques to efficiently process this type of data. In addressing this challenge, we proposed a hybrid data-driven feature tracking method that utilizes data from both event cameras and frame-based cameras to track features asynchronously. It mainly includes patch initialization, patch optimization, and patch association modules. In the patch initialization module, FAST corners are detected in frame images, providing points responsive to local brightness changes. The patch association module introduces a nearest-neighbor (NN) algorithm to filter new feature points effectively. The patch optimization module assesses optimization quality for tracking quality monitoring. We evaluate the tracking accuracy and robustness of our method using public and self-collected datasets, focusing on average tracking error and feature age. In contrast to the event-based Kanade–Lucas–Tomasi tracker method, our method decreases the average tracking error ranging from 1.3 to 29.2% and boosts the feature age ranging from 9.6 to 32.1%, while ensuring the computational efficiency improvement of 1.2–7.6%. Thus, our proposed feature tracking method utilizes the unique characteristics of event cameras and traditional cameras to deliver a robust and efficient tracking system.

Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma

Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.

Physically based simulation of focusing schlieren imaging for a hypersonic boundary layer flow.

Focusing schlieren systems are more advantageous than conventional schlieren systems in providing a schlieren image with certain spatial discrimination along the light path. The present work employed a hybrid of the optical-transfer matrix and ray-tracing method to faithfully replicate complete physical imaging processes throughout a focusing schlieren optic system. A direct numerical simulation of a hypersonic boundary layer flow was employed to synthesize focusing schlieren images. The influence of various configuration parameters on the properties of focusing schlieren image such as local schlieren structure, brightness, sensitivity, and depth of field were systematically explored. In addition, an approximation method was proposed as a simplified means to facilitate the simulation of a focusing schlieren image.

Low-Light Image Enhancement by Retinex-Based Algorithm Unrolling and Adjustment.

Low-light image enhancement (LIE) has attracted tremendous research interests in recent years. Retinex theory-based deep learning methods, following a decomposition-adjustment pipeline, have achieved promising performance due to their physical interpretability. However, existing Retinex-based deep learning methods are still suboptimal, failing to leverage useful insights from traditional approaches. Meanwhile, the adjustment step is either oversimplified or overcomplicated, resulting in unsatisfactory performance in practice. To address these issues, we propose a novel deep-learning framework for LIE. The framework consists of a decomposition network (DecNet) inspired by algorithm unrolling and adjustment networks considering both global and local brightness. The algorithm unrolling allows the integration of both implicit priors learned from data and explicit priors inherited from traditional methods, facilitating better decomposition. Meanwhile, considering global and local brightness guides the design of effective yet lightweight adjustment networks. Moreover, we introduce a self-supervised fine-tuning strategy that achieves promising performance without manual hyperparameter tuning. Extensive experiments on benchmark LIE datasets demonstrate the superiority of our approach over existing state-of-the-art methods both quantitatively and qualitatively. Code is available at https://github.com/Xinyil256/RAUNA2023.

Edge Bleeding Artifact Reduction for Shape from Focus in Microscopic 3D Sensing.

Shape from focus enables microscopic 3D sensing by combining it with a microscope system. However, edge bleeding artifacts of estimated depth easily occur in this environment. Therefore, this study analyzed artifacts and proposed a method to reduce edge bleeding artifacts. As a result of the analysis, the artifact factors are the depth of field of the lens, object texture, brightness difference between layers, and the slope of the object. Additionally, to reduce artifacts, a weighted focus measure value method was proposed based on the asymmetry of local brightness in artifacts. The proposed reduction method was evaluated through simulation and implementation. Edge bleeding artifact reduction rates of up to 60% were shown in various focus measure operators. The proposed method can be used with postprocessing algorithms and reduces edge bleeding artifacts.

Low illumination image enhancement based on attention mechanism and global illumination estimation

Aiming at the problems of insufficient or excessive local brightness enhancement, color distortion, and excessive noise in the existing low-light image enhancement algorithms, a low-light image enhancement method combining attention mechanism and global illumination estimation is proposed. First, the illumination distribution map of low illumination image is obtained through the illumination distribution estimation network coupled with the attention gate mechanism. Then, the weight of the light distribution map is learned in the feature attention module. Finally, the image details are fused by the detail reconstruction module to create improved image. According to the experimental results, this method may effectively improve image brightness, contrast, and color in subjective visual effects while also enhancing objective evaluation indicators like PSNR, SSIM, and MSE when compared to some conventional methods.

Skyglow relieves a crepuscular bird from visual constraints on being active

Artificial light at night significantly alters the predictability of the natural light cycles that most animals use as an essential Zeitgeber for daily activity. Direct light has well-documented local impacts on activity patterns of diurnal and nocturnal organisms. However, artificial light at night also contributes to an indirect illumination of the night sky, called skyglow, which is rapidly increasing. The consequences of this wide-spread form of artificial night light on the behaviour of animals remain poorly understood, with only a few studies performed under controlled (laboratory) conditions. Using animal-borne activity loggers, we investigated daily and seasonal flight activity of a free-living crepuscular bird species in response to nocturnal light conditions at sites differing dramatically in exposure to skyglow. We find that flight activity of European Nightjars (Caprimulgus europaeus) during moonless periods of the night is four times higher in Belgium (high skyglow exposure) than in sub-tropical Africa and two times higher than in Mongolia (near-pristine skies). Moreover, clouds darken the sky under natural conditions, but skyglow can strongly increase local sky brightness on overcast nights. As a result, we find that nightjars' response to cloud cover is reversed between Belgium and sub-tropical Africa and between Belgium and Mongolia. This supports the hypothesis that cloudy nights reduce individual flight activity in a pristine environment, but increase it when the sky is artificially lit. Our study shows that in the absence of direct light pollution, anthropogenic changes in sky brightness relieve nightjars from visual constraints on being active. Individuals adapt daily activities to artificial night-sky brightness, allowing them more time to fly than conspecifics living under natural light cycles. This modification of the nocturnal timescape likely affects behavioural processes of most crepuscular and nocturnal species, but its implications for population dynamics and interspecific interactions remain to be investigated.

Target Detection for Synthetic Aperture Radiometer Based on Satellite Formation Flight.

Synthetic aperture interferometers formed by satellite formations have been adopted to improve spatial resolution. Due to the limited number of satellites and limited integrated time, the use of sparse baselines can result in distorted reconstructed images, which will generate false targets or miss true targets. When detecting a target on the Earth from a geostationary orbit, the target usually occupies only one pixel, and it is almost submerged by noise. Considering the slow-varying characteristics of the observation area, combined with historical observation data and the motion characteristics of the target itself, a target detection method based on multi-frame snapshot images is proposed. Firstly, the observation background is estimated using multi-frame historical data, and background elimination is used to suppress the background noise. Then, potential targets are selected using the local brightness temperature characteristics of the targets. Lastly, the target motion tracks are applied to erase false targets and correct the positions of missed targets. Simulation experiments have been conducted, and the false alarm rate and the missing alarm rate are counted for randomly distributed targets.

Effects of Stimulus Luminance, Stimulus Color and Intra-Stimulus Color Contrast on Visual Field Mapping in Neurologically Impaired Adults Using Flicker Pupil Perimetry.

We improve pupillary responses and diagnostic performance of flicker pupil perimetry through alterations in global and local color contrast and luminance contrast in adult patients suffering from visual field defects due to cerebral visual impairment (CVI). Two experiments were conducted on patients with CVI (Experiment 1: 19 subjects, age M and SD 57.9 ± 14.0; Experiment 2: 16 subjects, age M and SD 57.3 ± 14.7) suffering from absolute homonymous visual field (VF) defects. We altered global color contrast (stimuli consisted of white, yellow, cyan and yellow-equiluminant-to-cyan colored wedges) in Experiment 1, and we manipulated luminance and local color contrast with bright and dark yellow and multicolor wedges in a 2-by-2 design in Experiment 2. Stimuli consecutively flickered across 44 stimulus locations within the inner 60 degrees of the VF and were offset to a contrasting (opponency colored) dark background. Pupil perimetry results were compared to standard automated perimetry (SAP) to assess diagnostic accuracy. A bright stimulus with global color contrast using yellow (p=0.009) or white (p=0.006) evoked strongest pupillary responses as opposed to stimuli containing local color contrast and lower brightness. Diagnostic accuracy, however, was similar across global color contrast conditions in Experiment 1 (p=0.27) and decreased when local color contrast and less luminance contrast was introduced in Experiment 2 (p=0.02). The bright yellow condition resulted in highest performance (AUC M =0.85±0.10, Mdn=0.85). Pupillary responses and pupil perimetry's diagnostic accuracy both benefit from high luminance contrast and global but not local color contrast.

An approach for improving Optical Character Recognition using Contrast enhancement technique

Digital cameras and mobile image capture of documents are two examples of new developments in the fields of optical character recognition and text recognition. Scans of text or text photographic images and even natural photography results can be distorted to the point where OCR digitization is inaccurate. It offers a unique non-parametric unattended approach to correct unwanted document image distortions to achieve optimal OCR accuracy. It applies a highly effective stack of document image enhancement algorithms to restore perfect images distorted by unknown sources of distortion. First, it provides a means of modifying local brightness and contrast in order to better handle different illumination levels and atypical light transmission patterns in the image. Then apply a nifty grayscale conversion method to your photo to give it a new look. Third, it uses unsharp masking techniques to further enhance important details in grayscale images. Finally, we use the best global binarization technique to prepare the final document image for OCR recognition. The proposed technique has the potential to significantly improve the text recognition rate and accuracy of optical character recognition.

Fractal and statistical characterization of brushstroke on paintings

Identification of an individual artist’s touch on paintings is studied using surface metrology. Paintings’ topographies were measured using focus variation and stitching, creating 13 × 13 mm maps with 1 μm sampling intervals, and 169 megapixels, with a 10X objective lens. Topographic characterization parameters were analyzed for their ability to differentiate different painters’ renderings. Statistical treatments from data mining were used to discriminate, by optimization, multiscale topographic signatures characterized by a multitude of areal texture parameters. It appears that a fractal dimension can define 3 characteristic scale ranges. One from 3 to 70 μm corresponds to brushstroke details. Another, from 70 to 700 μm, corresponds to the topography of the material of the canvas fabric. Finally, scales greater than 700 μm correspond to undulations of the canvas. For scales less than 50 μm, the fractal structure of the topography left by brushstrokes follows a power law characterized by the slopes of the topography. The topography of the clouds painted on the canvas has an Sdq (topographic slopes) increasing with the clarity of the clouds at scales of 3–500 μm. According to the Torrance-Sparrow theory, the higher the Sdq, the more diffuse the light on the surface. The painter therefore wanted to show, by his brushstroke, that the light clouds diffuse more light giving an impression of local brightness. This study is confirmed by the analysis of the painting of Max Savy, a French painter from Carcassonne (1918–2009), which was measured with a white light interferometer Zygo NewView 7300, a X100 objective lens giving a 517 μm × 517 μm stitched surface, with a sampling interval of 0.109 μm. The box-counting method for estimating the fractal dimension of the topography of an oil painting appears optimal by the fact that it morphologically integrates scale variations of the local slopes of the surface morphology. This method thus characterizes the multiscale aspects, as well as the scale changes, of the topography.

A Method for Retrieving Cloud-Top Height Based on a Machine Learning Model Using the Himawari-8 Combined with Near Infrared Data

Different cloud-top heights (CTHs) have different degrees of atmospheric heating, which is an important factor for weather forecasting and aviation safety. AHIs (Advanced Himawari Imagers) on the Himawari-8 satellite are a new generation of visible and infrared imaging spectrometers characterized by a wide observation range and a high temporal resolution. In this paper, a cloud-top height retrieval algorithm based on XGBoost is proposed. The algorithm comprehensively utilizes AHI L1 multi-channel radiance data and calculates the input parameters of the generated model according to the characteristics of the cloud phase, texture, and the local brightness temperature change of the cloud. In addition, the latitude, longitude, solar zenith angle and satellite zenith angle are input into the model to further constrain the influence of the geographical and spatial factors such as the sea and land location, on CTH. Compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud-top height data (CTHCAL), the results show that: the algorithm retrieved the cloud-top height (CTHXGB) with a mean error (ME) of 0.3 km, a standard deviation (Std) of 1.72 km, and a root mean square error (RMSE) of 1.74 km. Additionally, it improves the problem of the large systematic deviation in the cloud-top height products released by the Japan Meteorological Agency (CTHJMA), especially for ice clouds and multi-layer clouds with ice clouds on the top layer. For water clouds below 2 km and multi-layer clouds with water clouds at the top, the algorithm solves the systematically serious CTHJMA problem. XGBoost can effectively distinguish between different cloud scenarios within the model, which is robust and suitable for CTH retrieval.

Enhancing UAV images to improve the observation of submerged whales using a water column correction method

Enhancing <scp>UAV</scp> images to improve the observation of submerged whales using a water column correction method

Research on Retinex Algorithm Combining with Attention Mechanism for Image Enhancement

Considering the high noise and chromatic aberration in the Retinex-Net image enhancement results, this paper put forward a modified Retinex-Net algorithm for weak illumination image enhancement based on the Decom-Net and Enhance-Net structures of Retinex-Net. The improved structure proposed in this paper adds the attention mechanism ECA-Net into the Decom-Net and Enhance-Net convolution layer of the original Retinex-Net structure, which can effectively reduce the problem of irrelevant background and local brightness imbalance, activate sensitive features, and improve the image’s details and brightness processing ability. Additionally, deep connected attention networks are embedded between the introduced attention modules, so that all of the attention modules can be trained jointly to improve the learning ability. Furthermore, the improved method also introduces a noise reduction loss function and a color loss function to suppress noise and to reduce image color distortion. The test results of the proposed method indicate that the image’s overall brightness can be balanced, the local areas cannot be overexposed, and more image details and color information can be retained than with other enhancement algorithms.

Eliminating brightness induction effects when measuring motion centre-surround suppression of contrast

The perceived contrast of a central stimulus is supressed when it is embedded in a higher contrast surround, centre-surround suppression of contrast. Local brightness induction effects between the two stimulus regions have been proposed to account for conflicting results when relative grating phases were different. Here, suppression and brightness induction effects are dissociated using a centre-surround arrangement with moving gratings. Four experienced observers were involved in experiments, utilising two-interval forced-choice contrast matching tasks. The stimuli were drifting sinusoidal grating patterns with surrounds (95% contrast) differing in direction of motion and orientation relative to the 40% contrast centre grating. First a 90°-phase-offset same direction surround condition was compared to both same direction (phase aligned) and opposing direction conditions. The reduction in the suppression for the phase-offset condition suggested a reduction in brightness induction influences. Then suppression was examined when surround directions varied and where phase was either fixed or randomised. For small changes in the motion direction between centre and surround (0° to 26.6°) the amount of brightness induction varied sinusoidally with the difference in phase introduced by the direction difference. Finally, the spatial separation between the centre and surround was varied to determine the reduction of suppression and brightness induction with increasing spatial distance. We found both fit an exponential decay function, with surround suppression producing the larger range of influence. Our findings quantify both brightness induction and suppression effects and validate the use of phase randomisation to remove effects of brightness induction when evaluating surround suppression.

Radiomic Study for Objectification of Diagnostics and Complex Treatment of Glioblastoma

Introduction.Glioblastoma is a neuroepithelial malignant brain tumour of predominantly astrocytic origin with an aggressive course and an extremely unfavorable prognosis. Since the median of overall survival with glioblastoma is 14.6 months after complex treatment that includes a combination of surgical treatment, radiation therapy and chemotherapy, the development a personalized approach in the diagnosis and treatment of glioblastomas is appeared to be urgent.Materials and methods. MRIs of a patient undergoing chemoradiotherapy for glioblastoma G4 were performed on the following MRI scanners: Philips Ingenia 1.5T and Philips Ingenia Ambient 1.5T. The analysis of MR-images was carried out using the Matlab 2021 apps.Results and discussion.MR-images were analyzed before and after surgery, and after a course of chemoradiotherapy. The statistical characteristics of the local brightness distribution of the lesion image, which are described by statistical texture parameters, were analyzed as informative features of the lesion area on the images. Initial confirmation of the ability to objectify diagnosis and treatment using the above statistical parameters of T2 MR images of lesion area has been obtained.Conclusion.The aim of further research in this area is to use radiomic study for planning and monitoring the treatment of high-grade gliomas, estimate disease outcomes, and analyze the response to complex treatments in a predictive way.

CRISPR FISHer enables high-sensitivity imaging of nonrepetitive DNA in living cells through phase separation-mediated signal amplification

The dynamic three-dimensional structures of chromatin and extrachromosomal DNA molecules regulate fundamental cellular processes and beyond. However, the visualization of specific DNA sequences in live cells, especially nonrepetitive sequences accounting for most of the genome, is still vastly challenging. Here, we introduce a robust CRISPR-mediated fluorescence in situ hybridization amplifier (CRISPR FISHer) system, which exploits engineered sgRNA and protein trimerization domain-mediated, phase separation-based exponential assembly of fluorescent proteins in the CRISPR-targeting locus, conferring enhancements in both local brightness and signal-to-background ratio and thus achieving single sgRNA-directed visualization of native nonrepetitive DNA loci in live cells. In one application, by labeling and tracking the broken ends of chromosomal fragments, CRISPR FISHer enables real-time visualization of the entire process of chromosome breakage, separation, and subsequent intra- or inter-chromosomal ends rejoining in a single live cell. Furthermore, CRISPR FISHer allows the movement of small extrachromosomal circular DNAs (eccDNAs) and invading DNAs to be recorded, revealing substantial differences in dynamic behaviors between chromosomal and extrachromosomal loci. With the potential to track any specified self or non-self DNA sequences, CRISPR FISHer dramatically broadens the scope of live-cell imaging in biological events and for biomedical diagnoses.