Smooth Intensity Research Articles

De-Epithelization of the Human Amniotic Membrane Using a System Involving Ozonated Water and Ultrasound.

The aim of this study was to evaluate whether a system involving ozonated water and ultrasound causes de-epithelization of the human amniotic membrane (HAM). The experiment protocol was carried out in four stages. Stage I was carried out to determine the duration of the experiment. Stage II comprised the first experiment, involving four groups of samples studied in triplicate: control/natural (IN), processed with ultrasound in a liquid medium (US), processed with ozonated water (O3), and processed with ozonated water combined with ultrasound (US_O3). Stage III was performed to confirm the results, following the same steps present in Stage II. Stage IV involved the use of oxygen to confirm the hypothesis. Histological analysis was carried out to verify whether the effects of O2 were similar to those of O3. The system was activated, and ozonation was carried out for 10 min, as in the previous experiment, reaching a concentration level of 3.0 mg/L. The samples were submerged and positioned in the reservoir and processed separately for 55 min. The biochemical properties were assessed using Fourier transform infrared spectroscopy, and the morphology was examined using histology and scanning electron microscopy. The spectra of the samples exhibited similarities; however, subtle changes were highlighted, such as smooth band shifts and intensity changes. The morphology indicated that ultrasound achieved more efficient HAM de-epithelialization compared to ultrasound combined with ozonated water and ozonated water alone. One plausible hypothesis for this observation is that cavitation represents the primary mechanism responsible for de-epithelialization. When ultrasound is combined with ozone, the bubbles generated by ozone gas reduce the cavitation effect. This study is pioneering as it demonstrates an ultrasound system capable of the efficient de-epithelialization of the HAM.

Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.

Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. Keywords: generalized linear models; meta-analysis; spatial statistics; statistical modeling.

A Weighted Adaptive Range-Averaging Technique to Improve the Precision Consistency of Polarimetric Variable Fields

Abstract The two main metrics for the performance evaluation of radar-variable estimators are the bias and standard deviation (SD) of estimates. Depending on the estimator properties, the bias may increase as the signal-to-noise ratio (SNR) decreases. The standard deviation, however, always rises as the SNR becomes smaller. For instance, if estimates are computed from 16 samples (typically used for WSR-88D surveillance scans) using a rectangular data window and the maximum unambiguous velocity is ∼9 m s−1, the standard deviation of reflectivity estimates increases 1.6 times as the SNR drops from 20 to 2 dB. But for estimates of differential reflectivity, differential phase, and copolar correlation coefficient, SDs increase ∼6.7, ∼6, and ∼54 times, respectively. Hence, this effect impacts the polarimetric variables substantially more than the spectral moments. Additionally, the polarimetric variable SD is also sensitive to the correlation between signals in horizontal and vertical channels leading to reduced data quality in the regions where the correlation coefficient is low. Such increases in the variability of estimates are observable in the fields of dual polarization variables as an increased spatial inhomogeneity (or noisiness) in the areas where radar echoes exhibit low-to-moderate SNRs and/or decreased correlation coefficient. These effects can obscure the visual identification of weather features as well as adversely impact algorithms. Herein, a novel method that applies variable smoothing in the range where the smoothing intensity depends on the SDs of estimates is presented. It applies little or no range averaging in the regions where data SDs are deemed adequate while using more aggressive smoothing in areas where data appear noisy. Significance Statement The noisiness in the fields of polarimetric variables is an issue that has plagued dual-polarization weather radars since their inception. This is because standard deviations of polarimetric variable estimates increase significantly more with the reduction in SNR than those of spectral moment estimates. A typical mitigation approach that indiscriminately averages a fixed number of estimates in the range may lead to unnecessary loss of range resolution in regions where data appearance is satisfactory. Further, such an approach can be inadequate in regions with a high variability of estimates leading to insufficient enhancement of weather feature visibility. In this study, a method that mitigates these issues is proposed.

Data assimilation in 2D hyperbolic/parabolic systems using a stabilized explicit finite difference scheme run backward in time

ABSTRACTAn artificial example of a coupled system of three nonlinear partial differential equations generalizing 2D thermoelastic vibrations, is used to demonstrate the effectiveness, as well as the limitations, of a non iterative direct procedure in data assimilation. A stabilized explicit finite difference scheme, run backward in time, is used to find initial values, [u(.,0),v(.,0),w(.,0)], that can evolve into a useful approximation to a hypothetical target result [u∗(.,Tmax),v∗(.,Tmax),w∗(Tmax)], at some realistic 0 $ ]]>Tmax>0. Highly non smooth target data are considered, that may not correspond to actual solutions at time Tmax. Stabilization is achieved by applying a compensating smoothing operator at each time step. Such smoothing leads to a distortion away from the true solution, but that distortion is small enough to allow for useful results. Data assimilation is illustrated using 512×512 pixel images. Such images are associated with highly irregular non smooth intensity data that severely challenge ill-posed reconstruction procedures. Computational experiments show that efficient FFT-synthesized smoothing operators, based on (−Δ)q with real q>3, can be successfully applied, even in nonlinear problems in non-rectangular domains. However, an example of failure illustrates the limitations of the method in problems where Tmax, and/or the nonlinearity, are not sufficiently small.

A new learning space-variant anisotropic constrained-PDE for image denoising

In this paper, we propose an improved enhancement space-variant anisotropic PDE-constrained for image denoising, based on a learning optimization procedure. Since the tensor structure of the Weickert-type operators encodes three critical parameters: λ1,λ2 and θ, which define the local direction geometry in the image and also controls smoothing intensity along image features. We have then adopted an automatic estimation of these parameters based on a PDE-constrained optimization with a learning step of the additional information about the clean image. For the numerical solution of the computed free-noise image, we introduce a non-smooth Alternating Direction Method of Multipliers (ADMM) algorithm. The numerous tests in the experiments part incense, visually and quantitatively, the incomes of this new spatial tensor for PDE smoothing over the other denoising approaches.

Robust Training of Neural Networks against Bias Field Perturbations

We introduce the problem of training neural networks such that they are robust against a class of smooth intensity perturbations modelled by bias fields. We first develop an approach towards this goal based on a state-of-the-art robust training method utilising Interval Bound Propagation (IBP). We analyse the resulting algorithm and observe that IBP often produces very loose bounds for bias field perturbations, which may be detrimental to training. We then propose an alternative approach based on Symbolic Interval Propagation (SIP), which usually results in significantly tighter bounds than IBP. We present ROBNET, a tool implementing these approaches for bias field robust training. In experiments networks trained with the SIP-based approach achieved up to 31% higher certified robustness while also maintaining a better accuracy than networks trained with the IBP approach.

Generalized central slice theorem perspective on Fourier-transform spectral imaging at a sub-Nyquist sampling rate.

Fourier-transform spectral imaging captures frequency-resolved images with high spectral resolution, broad spectral range, high photon flux, and low stray light. In this technique, spectral information is resolved by taking Fourier transformation of the interference signals of two copies of the incident light at different time delays. The time delay should be scanned at a high sampling rate beyond the Nyquist limit to avoid aliasing, at the price of low measurement efficiency and stringent requirements on motion control for time delay scan. Here we propose, what we believe to be, a new perspective on Fourier-transform spectral imaging based on a generalized central slice theorem analogous to computerized tomography, using an angularly dispersive optics decouples measurements of the spectral envelope and the central frequency. Thus, as the central frequency is directly determined by the angular dispersion, the smooth spectral-spatial intensity envelope is reconstructed from interferograms measured at a sub-Nyquist time delay sampling rate. This perspective enables high-efficiency hyperspectral imaging and even spatiotemporal optical field characterization of femtosecond laser pulses without a loss of spectral and spatial resolutions.

1D spatially chirped periodic structures: managing their spatial spectrum and investigating their near-field diffraction.

This work introduces a class of 1D spatial-frequency-modulated structures with transmittance T(x), in which the period changes along the x axis so that the corresponding spatial frequency f(x) sinusoidally alternates between two values. It is shown that T(x) generally is an almost-periodic function and has an impulsive spatial spectrum. However, we find the condition under which T(x) is a periodic function and its spatial spectrum form a lattice of impulses. When the periodicity condition is fulfilled, we call these structures as 1D spatially chirped periodic structures. These structures are characterized by two natural numbers, named as n c and n a v , and a real parameter named as frequency modulation strength (FMS). As an important special case, we define a 1D spatially chirped amplitude sinusoidal grating (SCASG) based on the transmission function of a conventional amplitude sinusoidal grating, in which the phase of conventional amplitude sinusoidal grating is replaced by desired chirped phase. Then the spatial spectrum of a 1D SCASG is investigated in detail, and it is shown that the spatial spectrum can be managed by changing the value of FMS. In other words, the grating's spectrum can be manipulated by adjusting the value of FMS. This feature might find applications in optical sharing of the incident power among different diffraction orders. Moreover, near-field diffraction from 1D SCASGs is studied by using the so-called angular (spatial) spectrum method, and Talbot distances for these gratings are determined and verified experimentally. It is shown that the intensity profiles at quartet- and octant-Talbot distances strongly depend on the values of the parameters n c and n a v . In comparison with the conventional gratings, we see some new and interesting aspects in the diffraction from 1D SCASGs. For instance, unlike the conventional gratings, in some propagation distances, the diffraction patterns possess sharp and smooth intensity bars at which the intensity is several times of the incident light beam's intensity. It is shown that the maximum intensity of these bright bars over the diffraction patterns depends on the characteristic parameters of the grating, including n c , n a v , and FMS of the grating. These intensity bars might find applications for trapping and aggregation of particles along straight lines.

Effectiveness, Attractiveness, and Emotional Response to Voice Pitch and Hand Gestures in Public Speaking

Some non-verbal cues, such as voice pitch and gestures, can influence the individual's emotional response to different messages. For this reason, non-verbal communication can be understood as the language of emotion, while the content is the language of ideas. But the research question here is what voice pitch and hand gesture strategies are more effective, attractive, and elicit a stronger emotional response. To answer this question, this study analyzes some pitch and hand gesture strategies in public discourses. In the experiment, 120 participants were exposed to different public presentations with three conditions for voice pitch (few, medium, and many variations) and three for hand gestures (smooth, medium, and strong intensity). Then, they rated the effectiveness and attractiveness levels and self-report arousal and valence while their emotional response was registered. Physiological arousal was measured with electrodermal activity (EDA) and valence with facial expression recognition. Participants perceived the medium pitch variations and the medium intensity of gestures as the most effective and attractive strategies with the highest self-reported arousal and valence. The combination that elicited the strongest autonomic arousal was medium pitch variations-strong gestures. Finally, the most positive emotional valence was for many pitch variations and medium gestures and the happiest for the combination many-strong.

Intensity estimation on geometric networks with penalized splines

In the past decades the growing amount of network data lead to many novel statistical models. In this paper we consider so-called geometric networks. Typical examples are road networks or other infrastructure networks. Nevertheless, the neurons or the blood vessels in a human body can also be interpreted as a geometric network embedded in a three-dimensional space. A network-specific metric, rather than the Euclidean metric, is usually used in all these applications, making the analyses of network data challenging. We consider network-based point processes, and our task is to estimate the intensity (or density) of the process which allows us to detect high- and low-intensity regions of the underlying stochastic processes. Available routines that tackle this problem are commonly based on kernel smoothing methods. This paper uses penalized spline smoothing and extends this toward smooth intensity estimation on geometric networks. Furthermore, our approach easily allows incorporating covariates, enabling us to respect the network geometry in a regression model framework. Several data examples and a simulation study show that penalized spline-based intensity estimation on geometric networks is a numerically stable and efficient tool. Furthermore, it also allows estimating linear and smooth covariate effects, distinguishing our approach from already existing methodologies.

Measuring the F-corona intensity through time correlation of total and polarized visible light images

We present a new correlation method for deriving the F-corona intensity distribution, which is based on the analysis of the evolution of the total and polarized visible light (VL) images. We studied the one-month variation profiles of the total and polarized brightness acquired with Large Angle Spectrometric COronagraph and found that in some regions they are highly correlated. Assuming that the F-corona does not vary significantly on a timescale of one month, we estimated its intensity in the high-correlation regions and reconstructed the corresponding intensity maps both during the solar-minimum and solar-maximum periods. Systematic uncertainties were estimated by performing dedicated simulations. We compared the resulting F-corona images with those determined using the inversion technique and found that the correlation method provides a smoother intensity distribution. We also obtained that the F-corona images calculated for consecutive months show no significant variation. Finally, we note that this method can be applied to the future high-cadence VL observations carried out with the Metis/Solar Orbiter coronagraph.

Low-dose Cone-Beam Computed Tomography Reconstruction through a fast Three-Dimensional Compressed Sensing Method Based on the Three-Dimensional Pseudo-polar Fourier Transform.

Background:Reconstruction of high quality two dimensional images from fan beam computed tomography (CT) with a limited number of projections is already feasible through Fourier based iterative reconstruction method. However, this article is focused on a more complicated reconstruction of three dimensional (3D) images in a sparse view cone beam computed tomography (CBCT) by utilizing Compressive Sensing (CS) based on 3D pseudo polar Fourier transform (PPFT).Method:In comparison with the prevalent Cartesian grid, PPFT re gridding is potent to remove rebinning and interpolation errors. Furthermore, using PPFT based radon transform as the measurement matrix, reduced the computational complexity.Results:In order to show the computational efficiency of the proposed method, we compare it with an algebraic reconstruction technique and a CS type algorithm. We observed convergence in <20 iterations in our algorithm while others would need at least 50 iterations for reconstructing a qualified phantom image. Furthermore, using a fast composite splitting algorithm solver in each iteration makes it a fast CBCT reconstruction algorithm. The algorithm will minimize a linear combination of three terms corresponding to a least square data fitting, Hessian (HS) Penalty and l1 norm wavelet regularization. We named it PP-based compressed sensing-HS-W. In the reconstruction range of 120 projections around the 360° rotation, the image quality is visually similar to reconstructed images by Feldkamp-Davis-Kress algorithm using 720 projections. This represents a high dose reduction.Conclusion:The main achievements of this work are to reduce the radiation dose without degrading the image quality. Its ability in removing the staircase effect, preserving edges and regions with smooth intensity transition, and producing high-resolution, low-noise reconstruction results in low-dose level are also shown.

Spreading Diffusion-Restriction Events in the Gyrencephalic Brain After Subarachnoid Hemorrhage Revealed by Continuous Magnetic Resonance Imaging.

How widely spreading depolarizations (SDs) propagate through the gyrencephalic brain, including sulci and deeper cortical areas, remains an important clinical question. Here, we investigated SDs that occur spontaneously after subarachnoid placement of autologous blood clots in sulci of the juvenile swine brain. To investigate the three-dimensional spread of waves, animals underwent continuous diffusion-weighted magnetic resonance imaging (DW-MRI) for up to 6h following clot placement. SD is the mechanism of the cytotoxic edema of developing infarction that is diagnosed by DW-MRI, and DW-MRI also captures transient diffusion restriction caused by SD in less injured or healthy brains. Here, images (b = 0, 375, and 750) were acquired across five coronal slices with 1.25 × 1.25-mm in-plane resolution and 5-mm slice thickness, and the protocol was repeated every 6.83-9.15s. Spatial drift correction, temporal smoothing, and signal intensity normalization were applied to generate videos of diffusion signal intensity changes for each coronal slice. Review of video data from five animals revealed ten discrete events consisting of focal diffusion restriction that propagated through cerebral cortex. All events originated in the cortex surrounding the sulcal clot, either in the gyrus (n = 4) or in the sulcal depth (n = 6). In six cases, two to three independent waves spread simultaneously in medial, lateral, and antero-posterior directions. Waves traveled within sulcal walls, traversed the depths of sulci to re-emerge on the adjacent gyrus, and, in three cases, spread fully around the dorsolateral convexity. One event spread deep to olfactory regions along midline cortex, and no events were observed contralateral to the subarachnoid clot. Together, these results suggest that SDs in the injured gyrencephalic brain originate near the injury focus and can spread extensively through the cortex to wide and deep uninjured regions. These findings have implications for transient neurologic deficits in the neurocritically ill patient and relevance to patient monitoring and therapeutics.

Market-consistent valuation of natural catastrophe risk

Natural catastrophe risk is increasingly being covered through alternative capital instead of reinsurance. Since most such instruments do not trade in an active market, their ongoing valuation is a challenge. As a solution, we propose to exploit pricing information embedded secondary market catastrophe bond quotes. Specifically, we use a reduced form model to extract implied Poisson intensities from regularly observed prices. Next, we show that the intensities can be explained by time to maturity and modeled probability of first loss. Along these two dimensions, we estimate smooth intensity surfaces that allow investors to mark illiquid catastrophe risk positions to market.

Differentiation of Acquired Immune Deficiency Syndrome Related Primary Central Nervous System Lymphoma from Cerebral toxoplasmosis with Use of Susceptibility-Weighted Imaging and Contrast Enhanced 3D-T1WI

BackgroundWe aimed to investigate whether susceptibility-weighted imaging (SWI) and contrast-enhanced 3D-T1WI can differentiate Acquired Immune Deficiency Syndrome-Related Primary Central Nervous System Lymphoma (AR-PCNSL) from cerebral toxoplasmosis. MethodsThis was a prospective cohort study. 20 AIDS patients were divided into AR-PCNSL group (13 cases) and cerebral toxoplasmosis group (7 cases) based on pathology results. We analyzed the appearance of lesions on SWI and enhanced 3D T1WI and ROC curves in the diagnosis of AR-PCNSL and cerebral toxoplasmosis. ResultsCerebral toxoplasmosis was more likely to show annular enhancement (p = 0.002) and complete smooth ring enhancement (p = 0.002). It was also more likely to present a complete, smooth low signal intensity rim (LSIR) (p = 0.002) and an incomplete, smooth LSIR (p = 0.019) on SWI. AR-PCNSL was more likely to present an incomplete, irregular LSIR (p < 0.001) and irregular central low signal intensity (CLSI) (p<0.001) on SWI. The areas under the ROC curve of the SWI-ILSS grade and enhanced volume on 3D-T1WI were 0.872 and 0.862, respectively. ConclusionA higher SWI-ILSS grade and larger 3D-T1WI volume enhancement were diagnostic for AR-PCNSL. SWI and CE 3D-T1WI were useful in the differential diagnosis of AR-PCNSL and cerebral toxoplasmosis.

Generation of an optical needle beam with a laser inscribed Pancharatnam-Berry phase element under imperfect conditions.

Beams exhibiting long focal lines and small focal spot sizes are desired in a variety of applications and are called optical needles, with Bessel beams being a common example. Conical prisms are regularly used to generate Bessel beams, however, this method is usually plagued by an appearance of on-axis oscillations. In this work, we consider an optical element based on the space-domain Pancharatnam-Berry phase (PBP) to generate a high-power optical needle with a smooth and constant on-axis intensity profile. The phase in PBP elements is not introduced through optical path differences but results from the geometric phase that accompanies space-variant polarization manipulation. Our implementation is based on a type 2 modification of bulk transparent glass material, resulting in the formation of nanogratings with slow axes aligned perpendicular to the grating corrugation. We investigate both numerically and experimentally the stability of an optical needle generation under imperfect conditions. Influences of misalignments in the optical schema are investigated numerically and experimentally.

Archeomagnetic intensity investigations of French medieval ceramic workshops: Contribution to regional field modeling and archeointensity-based dating

Seven new archeointensity data are obtained through the analysis of groups of pottery and kiln fragments from ceramic workshops unearthed in France, precisely dated from the High Middle Ages. The measurements are carried out using the Triaxe magnetometer, following a dedicated experimental protocol that takes into account the effects of anisotropy and cooling rate (CR) on thermoremanent magnetization acquisition. The new data are consistent with the evolution of intensity variations described by our previous data obtained in France and Northern Italy, which display between the 5th and 10th c. a pronounced camel-back shape. In particular, they provide supporting evidence of an intensity minimum that occurred around the transition between the 7th and 8th century. These data, combined with a selection of previously published results within a 700 km radius of Beaune and re-examined based on CR correction, formed the basis of new regional mean intensity variation curves based on two independent modeling approaches. The first algorithm developed by Thébault and Gallet (2010) based on bootstrapping and now irregularly spaced knots according to the data distribution gives rather smooth intensity variations, while the second approach proposed by Livermore et al. (2018) based on a transdimensional Bayesian technique shows more abrupt variations with sometimes stronger amplitudes. We explore the dating potential of these two variations curves, which have an unprecedented resolution, by studying two medieval pottery workshops. Six fragment groups (three per workshop) are analyzed using the Triaxe protocol, providing mean archeointensity values for each of the two sites. Two different procedures are used for their dating, either by comparing the intensity value to be dated with the reference intensity variation curves obtained from the two modeling techniques or by analyzing the marginal posterior probability distribution of the age values derived from the method of Livermore et al. (2018). For France, the two techniques yield very similar results. The archeointensity dating results combined with archeological arguments and radiocarbon data, make it possible to better constrain the age of the end of activity of the two workshops. Archeointensity investigation of displaced materials thus appears as an effective means to obtain original chronological constraints on the age of their production, paving the way for a wide range of complementary research on Medieval pottery.

Improving radiographic image contrast using multi layers of histogram equalization technique

&lt;span id="docs-internal-guid-43432eef-7fff-9949-6deb-865191ff0740"&gt;&lt;span&gt;Usually, X-ray image has distortion in many parts because it is focusing on bones rather than other, However, when dentist needs to make decision analysis, he does that by using X-ray and many opinions can be judged by looking closely on it like (inflammation, infection, tooth nerve, root of the tooth…). This paper proposes on new suggested technique by applying multilayers of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in order to make high contrast of X-ray, this technique provides very satisfied results and smooth intensity which leads to high clear X-ray image, by using Python3 and OpenCV.&lt;/span&gt;&lt;/span&gt;

Influence of Laser Beam Profiles on the Performance of Laser‐Driven Flyers

AbstractIn order to reveal the influence of laser beam profiles on the performance of laser‐driven flyers, three different laser beam profiles including original laser beam, reshaped laser beam and optical fiber output laser beam were used to launch the flyer plates. The original laser beam showed plenty of “hot spots”. A lenslet array was used to reshape the original laser beam and a much smoother intensity distribution was obtained. Both original and reshaped laser beams were coupled to optical fibers. Results showed that the laser energy that a fiber can reliably transmit increased 29 % and reached up to 59 mJ by reshaping the original laser beam. Furthermore, PDV measurements indicated that there was a 68 % increase in flyer velocity when the flyer was driven by a fiber coupled laser instead of the original laser, and the flyer velocity could reach up to 1.5 km/s when irradiated by a 50 mJ laser. Aside from the velocity increase, the fiber coupled laser was proven to be capable of producing controllable planar and intact flyer. A fiber coupled laser initiation system incorporating a laser slapper detonator was developed. The fine grain CL‐20 was successfully initiated using the fiber coupled laser initiation system under 40 mJ laser energy, implicating the fiber coupled system is applicable in high energy explosive initiations.

A fuzzy and spline based dynamic histogram equalization for contrast enhancement of brain images

AbstractIn general, medical images acquired at insufficient lighting conditions are suffering from low contrast issues which are inadequate for image analysis steps. One standard solution to improve the image contrast is to change its intensity distribution with the help of an image histogram. The histogram‐based contrast enhancement methods treat the images as regions rather than objects which would be more useful for applications like brain image enhancement. In this paper, a Fuzzy and Spline based Histogram Equalization (FSDHE) is proposed to perform contrast enhancement with medical images. The proposed FSDHE method partitions the image into connected components, and the type of components are identified with a fuzzy membership function. The dynamic histogram equalization is applied to each component individually. The equalized sub‐histograms are combined to drive the global histogram, which is inconsistent as dynamic histogram equalization treated the intensity range for each connected component differently. Hence, a spline‐based histogram smoothing is proposed here in this research work. The equalized intensity mapping is received as control points for the polynomial curve, and a smooth intensity transformation is interpolated as a spline curve. The proposed FSDHE model is analyzed with MRI‐brain image dataset of 3064 images, which consists of both benign and malignant cases. The contrast enhancement performance of the proposed FSDHE method is quantified by various measures like Absolute Mean Brightness Error (AMBE), Peak Signal to Noise Ratio (PSNR), Contrast (C), Weber Contrast (WC), Entropy, Hausdorff Distance (HD) and Texture Preservation (TP) measures. The performance of the FSDHE method is compared against with other histogram equalization methods, and the results indicate that the FSDHE method achieves better quality measures of 3.1401, 30.5499, 21.5486, 0.7779, 4.0252, 0.2777, and 0.7836 for the seven‐performance metrics.