Changes In Pixel Intensity Research Articles

Hybrid Algorithm (DES-Present) for encryption image color using 2D-Chaotic System

In recent years, image encryption algorithms have been developed to protect against unauthorized persons and maintain the privacy of recipients. Therefore, it is very important to choose the right algorithm and build it in a way that meets security and performance requirements. The DES algorithm is slow in encryption and decryption operations and vulnerable to attacks. There is a lightweight block cipher for devices with limited resources. May be more vulnerable than differential cryptanalysis. To achieve security, the key of the two algorithms (DES-Present) was generated using a two-dimensional chaos equation system. DES is applied twice for four rounds, and within each single round of the DES algorithm, the current lightweight algorithm is executed for only four rounds. The performance evaluation of the proposed algorithm was measured through several metrics, where a PSNR of 8.985941indicates that the image quality did not change after the encryption procedure, as do MSE 8945.715332and MAE 77.625417. The NPCR value of 99.66891% indicates a high degree of accuracy in changing pixel values. In addition, the uniform average change intensity (UACI) of only 22.0046% shows that the algorithm is good at making large changes in pixel intensity. Correlation coefficient test: horizontal 0.002574; diagonal -0.003017; vertical -0.035860. The results showed that the proposed algorithm is good for encrypting color images, which makes it suitable for use in various applications that need security.

A semi-automatic cardiovascular annotation and quantification toolbox utilizing prior knowledge-guided feature learning

Annotation and quantification of cardiovascular in intravascular ultrasound (IVUS) images are of great significance in assisting the clinical diagnosis and treatment of coronary heart disease. However, it is time-consuming and expensive for cardiologists to manually annotate cardiovascular and calculate parameters in IVUS images using different software during the diagnosis. In this paper, we propose an IVUS analysis toolbox (SaCAQ) for semi-automatic cardiovascular annotation and quantification via prior knowledge-guided feature learning. The SaCAQ consists of two main modules: (1) a semi-automatic cardiovascular annotation module and (2) a cardiovascular quantification module. The semi-automatic cardiovascular annotation module can accurately segment the vascular wall by a IVUS images segmentation model (ISM-IVUS). The ISM-IVUS first extracts vascular morphological features and pixel intensity change features for the complex image segmentation by the expert prior knowledge feature extraction mechanism. Secondly, vascular wall is accurately segmented by the multi-scale feature extraction of attention U-Net. Moreover, the segmentation result checking mechanism is applied to verify the automatic segmentation result, which enhances the reliability of SaCAQ. Plaque regions of interest to doctors are detected by the rule-based plaque detection method, which can assist in the quantification task. Finally, image measurement methods and medically recognized calculation formulas are used in the cardiovascular quantification module to obtain clinical parameters, which significantly improves the confidence of the results. The SaCAQ is verified on our own dataset containing 5242 IVUS slice images and 383 sets of parameters. The experiments demonstrate that the SaCAQ achieves high accuracy of vascular wall segmentation: mIoU of 86.16%, Dice coefficient of 93.43%, and Hausdorff distance of 0.2171 mm. Moreover, ISM-IVUS has good segmentation speed and robustness. The calculated parameters show good correlation and p-values. All of these results indicate that the proposed SaCAQ provides an efficient, accurate, and reliable toolbox for clinical diagnosis of cardiovascular disease. The SaCAQ and user manual can be publicly available from https://github.com/zHwiz/SaCAQ.

FEHAT: efficient, large scale and automated heartbeat detection in Medaka fish embryos.

High-resolution imaging of model organisms allows the quantification of important physiological measurements. In the case of fish with transparent embryos, these videos can visualize key physiological processes, such as heartbeat. High throughput systems can provide enough measurements for the robust investigation of developmental processes as well as the impact of system perturbations on physiological state. However, few analytical schemes have been designed to handle thousands of high-resolution videos without the need for some level of human intervention. We developed a software package, named FEHAT, to provide a fully automated solution for the analytics of large numbers of heart rate imaging datasets obtained from developing Medaka fish embryos in 96-well plate format imaged on an Acquifer machine. FEHAT uses image segmentation to define regions of the embryo showing changes in pixel intensity over time, followed by the classification of the most likely position of the heart and Fourier Transformations to estimate the heart rate. Here, we describe some important features of the FEHAT software, showcasing its performance across a large set of medaka fish embryos and compare its performance to established, less automated solutions. FEHAT provides reliable heart rate estimates across a range of temperature-based perturbations and can be applied to tens of thousands of embryos without the need for any human intervention. Data used in this manuscript will be made available on request.

Integration of Fire Detection and Personnel Accountability System

Fire incidents in public spaces pose significant risks to life and property. Traditional fire detection systems often suffer from delays and inaccuracies, necessitating the development of advanced solutions. This paper presents a comprehensive system for integrating fire detection and personnel accountability in surveillance scenarios. With the increasing concerns about safety in public spaces, the need for effective systems to detect fires and ensure personnel safety has become paramount. Traditional methods rely on manual intervention or basic sensors, often resulting in delayed or inaccurate alerts. To address these challenges, our system employs advanced image processing and machine learning to enhance safety measures. By analyzing surveillance camera feeds, it detects changes in pixel intensity, smoke patterns, and heat signatures to identify potential fire incidents. Machine learning models, such as convolutional neural networks (CNNs), are trained to accurately classify fire and non-fire regions in video frames. Additionally, the system tracks personnel presence and movement, ensuring accountability within the surveillance area. Modular and scalable, our system integrates seamlessly with existing infrastructure, providing reliable fire detection and personnel monitoring. Experimental results demonstrate its effectiveness, contributing to enhanced safety in public spaces and facilitating prompt emergency responses.

Two-stage cross-fusion network for stereo event-based depth estimation

Drawing inspiration from biological retina, event camera uses dynamic vision sensor to asynchronously capture pixel intensity changes. Event camera provides a series of advantages, including high dynamic range, high temporal resolution, lack of motion blur, and low power consumption, which make it suitable candidate for depth estimation, specially in challenging scenarios with fast motion and low-lighting conditions. However, due to the sparse nature of event data, it is troublesome to estimate dense disparity map from them. Although it is plausible to estimate disparity map from edges of a structure from where most events are captured, estimating dense disparity is challenging in the areas where events rarely occur. In this study, we develop a stereo matching network for dense disparity estimation, which effectively fuses reconstructed image features with event to estimate disparity. To this end, we propose a two-stage cross-fusion network (TCFNet) to estimate disparity by fusing event and image features with two cascaded fusion mechanisms. First, we propose cross-SPADE fusion, based on the spatially-adaptive denormalization mechanism (SPADE), which modulates event features with image features, and image features with event features. In the second stage, the modulated features are further fused using the cross-gating mechanism, which uses the multi-axis gated MLP (gMLP). Experiments on both real and synthetic datasets indicate that the proposed two-stage cross-fusion network outperforms the state-of-the-art event-based stereo matching networks empirically and visually.

A cross-sectional study of explainable machine learning in Alzheimer's disease: diagnostic classification using MR radiomic features.

Alzheimer's disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD. In this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated. The model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively. These directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD.

Using video detection of snow surface movements to estimate weak layer crack propagation speeds

Abstract Dry-snow slab avalanches release due to crack propagation in a weak snow layer under a cohesive snow slab. Crack propagation speeds can provide insights into the potential size of avalanches and inform fracture and avalanche release models. Despite their importance, slope-scale crack speed measurements from real avalanches are limited. Further, most existing slope-scale measurements utilize the appearance of slab fractures on the snow surface. However, we have no evidence that the appearance of surface cracking is a good indicator of the weak layer crack propagation tip. Here we present a novel method to estimate crack propagation speed from snow surface movements in avalanche videos. Our technique uses changes in frame pixel intensity, allowing us to detect the location of weak layer cracks well before slab fractures appear on the snow surface. We use field experiments and numerical simulations to validate our method before applying it to five avalanches. Our estimates show that cracks propagate faster up and down the slope than in the cross-slope direction; this suggests that different propagation regimes likely govern crack propagation up/down the slope, cross-slope and in flat terrain.

YONGʻINNI VIDEOTASVIRDA RANGLI FILTRLASH BILAN INTENSIVLIK OʻZGARISHI ASOSIDA ANIQLASH

The paper reviews achievements made in the field of video analytics and computer vision, which enable automatic detection of fires, based on video data. Various algorithms have been implemented in sought for effective fire detection methods using video. As one of these, a color-based fire detection algorithm is being described. However, using only one color model for fi re detection is an inefficient approach. The paper proposes a method for estimating temporal changes in pixel intensity, which is used to retrieve information about fires from fire-like objects in a video image. This method helps to calculate the average intensity value in a sequence of shots. A special software has been developed in the Python programming language using the OpenCV (Open Source Computer Vision Library) library, and the corresponding findings have been gained in view to demonstrate the effectiveness of the proposed method.

Disorder within disordered regions of protein-free lipid bilayers.

Optical microscopy-based reports on protein-free lipid bilayers with heterogeneous compositions generally consider observed phase separations as thermodynamic endpoints; while MD simulations indicate kinetic characteristics of domain formations. Here, we study GUVs of POPC:DOPE (9:1) and POPC:DPPC (9:1), labeled with disordered-phase probe Liss-RhodPE (1/1000 w/w), using confocal imaging. In addition to confirming previously well-reported disorder and/or phase-separations in vesicular membranes of above compositions, we report the discovery of non-uniform probe distributions within disordered phases, indicating varying degrees of disorder (VDD) for the same vesicular membrane. We further examine dynamics of probe distributions in VDD regions of vesicular membranes. Regression analysis of time-dependent changes in average pixel intensities of probe molecules (avPIs) in different regions of interest (ROIs) allowed quantitative characterization of local (within ROIs) and global (overall vesicular membrane) motion of probe molecules within disordered phases. Optics-related artifacts (e.g., photobleaching, excitation and/or emission fluctuations) were ruled out by noting negligible variations in whole-vesicle avPIs with time. However, avPIs within certain ROIs showed considerable changes with time. In fact, in the same vesicular membrane, avPIs in different ROIs within disordered phases show (a) linear dependence on time (indicating diffusive motion), (b) dependence on square of time (indicating convective motion), and (c) other dependencies on time (e.g., logarithmic, exponential, power, 1/time etc.) - all clubbed together as non-diffusive-non-convective motions. Thus, we rigorously establish existence of further (at least three) levels of heterogeneity, based on dynamics of probe molecules, within disordered phases of protein-free lipid bilayers. Such heterogeneity has previously been ascribed only to biologically active membranes due to presence of membrane proteins. Interestingly, while diffusive motion may be linked to overall membrane viscosity, convective motion may be linked to concepts such as line-tension resulting from heterogeneous compositions.

Contactless skin perfusion monitoring with video cameras: tracking pharmacological vasoconstriction and vasodilation using photoplethysmographic changes

Objectives. Clinical assessment of skin perfusion informs prognosis in critically ill patients. Video camera monitoring could provide an objective, continuous method to monitor skin perfusion. In this prospective, interventional study of healthy volunteers, we tested whether video camera-derived photoplethysmography imaging and colour measurements could detect drug-induced skin perfusion changes. Approach. We monitored the lower limbs of 30 volunteers using video cameras while administering phenylephrine (a vasoconstrictor) and glyceryl trinitrate (a vasodilator). We report relative pixel intensity changes from baseline, as absolute values are sensitive to environmental factors. The primary outcome was the pre- to peak- infusion green channel amplitude change in the pulsatile PPGi waveform component. Secondary outcomes were pre-to-peak changes in the photoplethysmographic imaging waveform baseline, skin colour hue and skin colour saturation. Main results. The 30 participants had a median age of 29 years (IQR 25–34), sixteen (53%) were male. A 34.7% (p = 0.0001) mean decrease in the amplitude of the pulsatile photoplethysmographic imaging waveform occurred following phenylephrine infusion. A 30.7% (p = 0.000004) mean increase occurred following glyceryl trinitrate infusion. The photoplethysmographic imaging baseline decreased with phenylephrine by 2.1% (p = 0.000 02) and increased with glyceryl trinitrate by 0.5% (p = 0.026). Skin colour hue changed in opposite direction with phenylephrine (−0.0013, p = 0.0002) and glyceryl trinitrate (+0.0006, p = 0.019). Skin colour saturation decreased with phenylephrine by 0.0022 (p = 0.0002), with no significant change observed with glyceryl trinitrate (+0.0005, p = 0.21). Significance. Drug-induced vasoconstriction and vasodilation are associated with detectable changes in photoplethysmographic imaging waveform parameters and skin hue. Our findings suggest video cameras have great potential for continuous, contactless skin perfusion monitoring.

Abstract P1033: A Simple 2-D Assay For Measuring Contraction And Calcium Kinetics In Engineered Human Cardiomyocytes To Model Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is caused by the loss of the dystrophin protein, resulting in progressive muscle wasting, loss of ambulation, and early mortality in the second and third decades of life. While the skeletal muscle phenotype is most prominently associated with DMD, heart failure is the leading cause of death in DMD patients, and therapies remain limited. Advancements in stem cell-derived models and assays for heart disease have exciting translational potential for DMD, such as patient-specific modeling and drug screening. Contracting 2-D monolayers and organoids are standards of cardiac engineering, but measuring contraction is challenging due in part due to multi-axial alignment of immature sarcomeres. Complicated solutions to measure contraction in stem cell models use fluorescent beads to infer contraction, transgenic systems that express fluorescent proteins, 3-D scaffolding, and/or custom hardware and software. Here, we utilize a common microscope system used for measuring mature cardiomyocyte (IonOptix) and a commercially-available module (CytoMotion) that measures changes in pixel intensity to index contraction. This platform allows simultaneous measurement of contraction motion and calcium transients just like with adult mammalian cardiomyocytes. Using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) from a Duchenne muscular dystrophy (DMD) patient and a healthy relative, we demonstrate DMD hiPSC-CM exhibit impaired contraction and calcium handling. We have since further increased the output of our assay using a custom 24-well stimulation plate, and current studies are using this workflow to validate experimental therapies that show promise from mouse models of DMD.

Multitemporal Change Detection and Irregular Land Shape Area Measurement from Multispectral Sensor Images through BSO Algorithm

Pixel-based classification and area measurement play a vital role in satellite image processing. The accuracy in classification and area measurement is required for remote monitoring various regions such as water area, cultivation regions, reservoir water spread, and disease spread in cultivation area. Traditionally, Google Earth Pro-based area measurement has error in area measurement due to curvature nature and irregular land surface. Moreover, exact point identification on land surface on Earth pro is difficult due to frequent changes on the land surface. The problem in the land area measurement is mostly affected due to the man-made changes in that particular land area. In this paper, we solve land area measurement error problem through the automated single and multithresholding pixels of different iterations on the land surface by the BSO algorithm. The land cover region pixel intensity changes on the curvature region of land surface with respect to spatial and temporal variations which are identified through BSO optimization-based image segmentation for exact area measurement. For the experimentation of accurate measurement, land area images such as urban, semiurban, hill, and coastal region from LANDSAT and SENTINEL images for period 2016 to 2019 are taken for the land area measurement study. BSO enhances and segments the land regions such as road, building, water body, vegetation, bare land, hill, and coastal region of about 32% more than particle swarm optimization (PSO) algorithm. Furthermore, the urban land area measurement accuracy increases to about 97% than the irregular land surface area.

Approach to edge detection in medical imaging through histogram analysis and morphological gradient

Edge detection takes importance in image processing systems for computer-aided diagnosis, wheresharp changes in pixel intensity are analyzed to obtain fast and accurate information about regions ofinterest to the specialist. A method for feature enhancement and edge detection in medical imageswas developed using image processing by analyzing the pixel distribution histogram andmorphological gradient operation. Images from the MINI MIAS dataset and the COVID-CT datasetwere used. The method is based on image processing and is applied to mammography and chest CTimages, where blur filtering steps are accompanied by morphological gradient filtering, in addition toobtaining the threshold for edge detection by analyzing the point of maximum pixel concentrationaccording to the distribution histogram. The processing is presented in a graphical user interfacedeveloped in Python language. The method is validated by comparison with other edge detectiontechniques such as the Canny Algorithm, and with deep learning methods such as Holistically-NestedEdge Detection. The proposed method improves image quality in both mammograms and CT scanscompared to other techniques. It also presents the best performance considering internal and externaledge detection, as well as an average response time of 1.054 seconds and 2.63 % of Central Processing Unit requirement. The developed system is presented as a support tool for use in computer-aideddiagnosis processes due to its high efficiency in edge detection.

The impact of digital filters on the diagnosis of simulated root resorptions in digital radiographic systems.

To evaluate the influence of digital filters of intraoral radiographic systems on the diagnosis of simulated internal and external root resorptions and image quality. Internal root resorption (IRR) and external root resorption (ERR) were simulated in 34 teeth. For image acquisition, two radiographic systems were used: Digora Toto and VistaScan. All filters available in these systems were applied. Three observers scored the detection of root resorptions in a 5-point scale. The noise and the contrast-to-noise ratio (CNR) were calculated. The area under ROC curve, sensitivity, specificity, and accuracy were obtained. One-way ANOVA with Tukey's post hoc tests compared the diagnostic values, noise, and CNR between the filters (α = 0.05). For ERR, there were no significant differences in diagnostic values between the filters tested for both systems. For IRR, Original and Noise Reduction filters presented higher sensitivity than the Sharpen2 filter for images from Digora Toto, with no differences between the other groups. For VistaScan, there were no significant differences of diagnostic values between the groups studied. Noise values differed among the filters of both systems. The CNR of the filters differed only for the bone region for Digora Toto, while for VistaScan, both tooth and bone regions differed. Despite promoting changes in pixel intensities and affecting the noise level of the radiographic images, the digital filters of Digora Toto and VistaScan systems do not affect the diagnosis of internal or external root resorptions. Digital filters are common tools in digital radiographic systems and may be used by the professional without impairment in root resorptions diagnosis.

Toward Top-Down Just Noticeable Difference Estimation of Natural Images.

Just noticeable difference (JND) of natural images refers to the maximum pixel intensity change magnitude that typical human visual system (HVS) cannot perceive. Existing efforts on JND estimation mainly dedicate to modeling the diverse masking effects in either/both spatial or/and frequency domains, and then fusing them into an overall JND estimate. In this work, we turn to a dramatically different way to address this problem with a top-down design philosophy. Instead of explicitly formulating and fusing different masking effects in a bottom-up way, the proposed JND estimation model dedicates to first predicting a critical perceptual lossless (CPL) counterpart of the original image and then calculating the difference map between the original image and the predicted CPL image as the JND map. We conduct subjective experiments to determine the critical points of 500 images and find that the distribution of cumulative normalized KLT coefficient energy values over all 500 images at these critical points can be well characterized by a Weibull distribution. Given a testing image, its corresponding critical point is determined by a simple weighted average scheme where the weights are determined by a fitted Weibull distribution function. The performance of the proposed JND model is evaluated explicitly with direct JND prediction and implicitly with two applications including JND-guided noise injection and JND-guided image compression. Experimental results have demonstrated that our proposed JND model can achieve better performance than several latest JND models. In addition, we also compare the proposed JND model with existing visual difference predicator (VDP) metrics in terms of the capability in distortion detection and discrimination. The results indicate that our JND model also has a good performance in this task. The code of this work are available at https://github.com/Zhentao-Liu/KLT-JND.

Estimating the mechanical energy of histotripsy bubble clouds with high frame rate imaging

Mechanical ablation with the focused ultrasound therapy histotripsy relies on the generation and action of bubble clouds. Despite its critical role for ablation, quantitative metrics of bubble activity to gauge treatment outcomes are still lacking. Here, plane wave imaging was used to track the dissolution of bubble clouds following initiation with the histotripsy pulse. Information about the rate of change in pixel intensity was coupled with an analytic diffusion model to estimate bubble size. Accuracy of the hybrid measurement/model was assessed by comparing the predicted and measured dissolution time of the bubble cloud. Good agreement was found between predictions and measurements of bubble cloud dissolution times in agarose phantoms and murine subcutaneous SCC VII tumors. The analytic diffusion model was extended to compute the maximum bubble size as well as energy imparted to the tissue due to bubble expansion. Regions within tumors predicted to have undergone strong bubble expansion were collocated with ablation. Further, the dissolution time was found to correlate with acoustic emissions generated by the bubble cloud during histotripsy insonation. Overall, these results indicate a combination of modeling and high frame rate imaging may provide means to quantify mechanical energy imparted to the tissue due to bubble expansion for histotripsy.

What Role Does Trabecular Bone Score Play in Chronic Inflammatory Rheumatic Diseases?

Patients suffering from rheumatic inflammatory diseases, e.g., systemic sclerosis, rheumatoid arthritis, and ankylosing spondylitis, are at risk of low bone mass. Dual-energy X-ray Absorptiometry (DXA) is the traditional radiological measurement technique for bone mineral density (BMD). The recently developed trabecular bone score (TBS) enhances the skeletal information provided by standard BMD. It re-analyzes the spatial dynamics of pixel intensity changes in lumbar spine DXA images, defining a quantitative index, characterizing trabecular bone microarchitecture. It has been demonstrated that low TBS values are associated with an increased incidence of fractures in patients with rheumatic diseases. These methods used together for bone damage evaluation can be of value to identify individuals who will potentially fracture. The main scientific literature on the clinical aspects of osteoporosis, including the use of TBS in evaluating this pathology, are herein reported aimed at shedding light on the role trabecular bone score plays in chronic inflammatory rheumatic diseases.

Do image enhancement filters in complementary metal oxide semiconductor and photostimulable phosphor imaging systems improve the detection of fractured endodontic instruments in periapical radiography?

The aim of this study was to evaluate the influence of image enhancement filters on detection of fractured endodontic instruments and on image noise and pixel intensity. In total, 26 mandibular molars (68 root canals), divided into 4 groups (presence/absence of root canal filling, and presence/absence of a fractured endodontic instrument) were radiographed by using a complementary metal oxide semiconductor (CMOS) and a photostimulable phosphor (PSP) system. Images with 4 enhancement filters (Sharpen UM, Edges All, BC Low, and BC High) were compared with nonfiltered images. The area under the receiver operating characteristic curve (Az), sensitivity, and specificity in detection of fractured instruments were compared for each imaging condition. Means and standard deviations (SD) to measure noise and changes in pixel intensity were calculated. Analysis of variance was used to analyze the data. There were no significant differences in Az, sensitivity, and specificity values between nonfiltered and filtered images in detection of instruments in root canals without and with root canal filling (P ≥ .360). SD was reduced by filters in CMOS but not PSP radiographs. Sharpen UM caused more changes in the mean values in both systems. Filters did not improve diagnostic performance in detecting instruments. Noise and pixel intensity were affected differently.

Boosting of Denoising Effect with Fusion Strategy

Image denoising, a fundamental step in image processing, has been widely studied for several decades. Denoising methods can be classified as internal or external depending on whether they exploit the internal prior or the external noisy-clean image priors to reconstruct a latent image. Typically, these two kinds of methods have their respective merits and demerits. Using a single denoising model to improve existing methods remains a challenge. In this paper, we propose a method for boosting the denoising effect via the image fusion strategy. This study aims to boost the performance of two typical denoising methods, the nonlocally centralized sparse representation (NCSR) and residual learning of deep CNN (DnCNN). These two methods have complementary strengths and can be chosen to represent internal and external denoising methods, respectively. The boosting process is formulated as an adaptive weight-based image fusion problem by preserving the details for the initial denoised images output by the NCSR and the DnCNN. Specifically, we design two kinds of weights to adaptively reflect the influence of the pixel intensity changes and the global gradient of the initial denoised images. A linear combination of these two kinds of weights determines the final weight. The initial denoised images are integrated into the fusion framework to achieve our denoising results. Extensive experiments show that the proposed method significantly outperforms the NCSR and the DnCNN both quantitatively and visually when they are considered as individual methods; similarly, it outperforms several other state-of-the-art denoising methods.

Deriving Tidal Structure From Satellite Image Time Series

AbstractIn shallow coastal regions, tides often control the water flux, which in turn directs sediment transport, nutrient delivery, and geochemical gradients. However, tides in shallow areas are spatially heterogeneous, making it challenging to constrain the geographic structure of tidal phase and amplitude without extensive networks of tide gauges. We present a simple remote sensing method for deriving tidal structure from satellite time series. Our method is based on two observations: (1) Tidally driven variations in water depth can be detected as changes in pixel intensity in optical satellite imagery, and (2) repeating passes by an orbiting satellite capture a region at different phases of the tidal cycle. By stacking multiple satellite acquisitions of a shallow bank, we can compute the relative tidal phase and amplitude for each pixel location, thereby resolving a detailed map of tidal propagation and attenuation. While our method requires a set of local water‐depth measurements to calibrate the color‐to‐depth relationship and compute tidal amplitude (in meters), our method can produce spatial estimates of tidal phase and relative amplitude without any site‐specific calibration data. As an illustration of the method, we use Landsat imagery to derive the spatial structure of tides on the Great Bahama Bank, estimating tidal phase and amplitude with mean absolute errors of 15 min and 0.15 m, respectively.