Textured Layer Research Articles

Textured Stainless Steel as a Platform for Black Mg2Si/Si Heterojunction Solar Cells with Advanced Photovoltaic Performance.

This paper reports on a facile bottom-up method for the direct integration of a silicon (Si)-magnesium silicide (Mg2Si) heterojunction solar cell (HSC) with a textured rear reflector made of stainless steel (SS). Modified wet chemical etching and post processing of SS substrates resulted in the formation of both a rough surface texture and diffusion barrier layer, consisting of magnetite (Fe3O4) with reduced optical reflection. Then, Si, Mg2Si and CaSi2 layers were stepwise thermally evaporated onto the textured SS surface. No traces of Fe and Cr silicide phases were detected by Raman spectroscopy, confirming effective suppression of impurity diffusion from the SS to the upper layers at least at temperatures required for Si deposition, as well as Mg2Si and CaSi2 formation. The obtained black-SS/Fe3O4/Si/Mg2Si/CaSi2 sample preserved, to some extent, its underlying textured morphology and demonstrated an averaged reflection of 15% over the spectral range of 200–1800 nm, while its prototype HSC possessed a wideband photoresponse with a photoelectric conversion efficiency of 7.5% under AM1.5 illumination. Moreover, Si layers deposited alone onto a black-SS substrate demonstrated competitive antireflection properties compared with black Si (b-Si) obtained by traditional top-down etching approaches, and hybrid b-Si/textured-SS structures with a glue-bonded interlayer.

Single nighttime image dehazing based on unified variational decomposition model and multi-scale contrast enhancement

Most of existing dehazing methods are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light, glows and hazes. To obtain high-quality image under nighttime haze imaging conditions, we propose a single nighttime image dehazing framework based on a unified variational decomposition model and multi-scale contrast enhancement to simultaneously address these undesirable issues. First, a unified variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ ℓ1 norm to constrain the structure component, adopt ℓ0 sparsity term to enforce the piece-wise continuous of the residual of the gradients between the detail layer and the modified glow-free image, and use the Frobenius norm to estimate the noise layer. Next, the hazes in the structure layer are removed by inversing the physical model and the effective details in the texture layers are enhanced while the amplified noises are suppressed in a multi-scale fashion. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experiments demonstrate that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with state-of-the-art dehazing techniques.

CSDM: A Cross-Scale Decomposition Method for Low-Light Image Enhancement

Inadequate lighting condition is one of the main factors affecting image quality. For high-quality image display and vision applications, low-light image enhancement is an extremely challenging task. However, most existing methods are difficult to remove heavy noise in the low-light enhancement process effectively. To solve this problem, this paper presents a novel cross-scale decomposition method (CSDM) for low-light image enhancement. The main idea is to capture the small-scale texture and heavy noise, and preserve the large-scale structure by designing a Gaussian regularization based on a cross-scale relative relationship. An optimization objective function is established to transform image decomposition, low-light enhancement and noise suppression into a unified optimization framework. The framework uses sequential decomposition to estimate texture and structure layers. Firstly, the structure layer of the image is estimated, and an illumination prior is introduced to improve the contrast. Then the texture layer is estimated, and a low-rank prior is applied to eliminate noise. Finally, the structure and texture layers are combined to obtain a final enhanced image. Experiment results on public datasets and an industrial site demonstrate that the advantages of our method from both qualitative and quantitative perspectives.

Image Reconstruction Using Deep Neural Networks Models

Abstract: Image restoration is the process of restoring the original image. It can be challenging to eliminate image blur in a variety of contexts, including photography, radar imaging, and the removal of motion blur brought on by camera shaking. Image noise is unintentional signal that enters an image from a sensor,such as a thermal or electrical signal or an external factor like rain or snow. The image degradation may be caused by transmission noise, object motion, resolution restrictions, coding artefacts, camera shake, or a combination of these factors. In order to distinguish between HF and LF artefacts, image decomposition is employed to divide the deformed image into a texture layer and a structure layer (Low Frequency LF Component) The current approach utilises the frequency characteristics of various forms of artefactsthrough a configurable deep neural network structure. Therefore, by changing the architecture, the same method may be applied to a number of picture restoration tasks. A quality enhancement network that uses residual and recursive learning is suggested for decreasing the artefacts with comparable frequency characteristics. Residuallearning is used to enhance performance and speed up the training process. Recursive learning is used to both improve performance and drastically cut down on the amount of training parameters. This Project aims to build systems for reconstructing the old images from under sampled one and mismatched Pixels to form a proper image to increase its visible quality and its pixels quality by using a Deep Neural network Models and it can improve the integration of various feature representations from many photos. Result Shows Improved Training accuracy of 92%.When compared to the two-frame designs now in use, the multi-frame architecture will be used which prevents repetitive computations caused by multiple inferences when aligning multiple images

Infrared and visible image fusion for ship targets based on scale‐aware feature decomposition

Infrared (IR) and visible (VI) image fusion play an important role in improving the sea scene perception and ship target detection. Although there have been many studies on image fusion considering the sea scene characteristics, there has still been no adequate extraction method for detailed information of a ship target in a sea scene. To overcome this shortcoming, this paper proposes a fusion method based on a scale-aware feature decomposition, which can accurately extract features of a ship target at different scales. First, a hybrid feature decomposition method based on the scale-aware structure-preserving filter and Gaussian filter is designed. The proposed method separated source images into region, structure, and texture layers, and thus achieved a finer-scale division than traditional multiscale decomposition methods in the sea-clutter background. Then, further decomposition of the texture layer was performed by the shearlet transform to obtain the directional texture feature. According to the characteristics of each layer, different strategies were adopted for the fusion, and the weighting factor was used to obtain the final fusion result. Experimental results indicated that the proposed method could achieve better subjective and objective results than current state-of-the-art methods. This method introduces a scale-aware edge preserving filter to improve the ability of detail extraction and is suitable for image fusion of ship targets.

The suitability of physical and chemical properties of rocks for land reclamation in different subzones of the Ukrainian Steppe


 
 
 The study of the properties of disturbed soils and rocks makes it possible to establish the parameters of the natural fertility of the studied substrates, to detect limiting factors, and to determine a set of restoration measures. The tested overburden rocks with the largest stratigraphic share of the open-pit quarries of the Nikopol manganese and Kerch iron ore deposits were loamy-like loess (Quaternary) and grey-green clay (Neogene). There is a certain parametric relationship between texture, density, porosity, the structural and physical state of different models of artificial reclaimed profiles. The composition and properties of rocks of edaphic structures of technosoils differ according to zonal natural and climatic conditions of the subzones of the Ukrainian Steppe. Loess-like loam and grey-green clay of the Kerch iron ore deposit are distinguished by a higher bulk density, lower porosity, and wilting moisture compared to samples from the Nikopol manganese ore basin. The use of the rotor complex leads to mechanical destruction and an increase in the content of small particles of rocks. Self-compacting processes occur to a large extent in multilayer structures. This pattern is observed at the boundary of the backfill differentiated in texture layers of technosoils. This indicates the existence of a barrier that prevents the relationship of edaphic properties between substrata stratums. Optimal ratio of clay minerals provides a rather high capacity of grey-green clay for water absorption. The model of technosoil composed of grey-green clay differs in a larger number of water-resistant micro-aggregates. The best conditions for land reclamation are connected with including grey-green clay in two- and three-layer constructions of technosoils, providing a waterproofing effect.
 
 

Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network.

The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme.

Formation of CdO/CdS/textured-ZnO/ZnO heterostructures by chemical deposition

CdO/CdS/textured-ZnO/ZnO heterostructure was synthesized using a combination of electrochemical etching and chemical deposition methods. Electrochemical etching was used to form a textured ZnO layer. Chemical deposition of a solution containing CdCl2 was performed to prepare a CdS film with CdO nanoparticles on the surface. The obtained nanocomposite was characterized using SEM, EDX and Raman methods for structural, morphological and component studies. SEM images and Raman scattering showed the existence of cubic phase of CdO nanocrystals. The SEM study revealed the dispersion of 50-200 nm agglomerated nanostructures on the surface of CdS film.

High-Efficiency Crystalline Silicon-Based Solar Cells Using Textured TiO2 Layer and Plasmonic Nanoparticles.

A high-efficiency crystalline silicon-based solar cell in the visible and near-infrared regions is introduced in this paper. A textured TiO2 layer grown on top of the active silicon layer and a back reflector with gratings are used to enhance the solar cell performance. The given structure is simulated using the finite difference time domain (FDTD) method to determine the solar cell’s performance. The simulation toolbox calculates the short circuit current density by solving Maxwell’s equation, and the open-circuit voltage will be calculated numerically according to the material parameters. Hence, each simulation process calculates the fill factor and power conversion efficiency numerically. The optimization of the crystalline silicon active layer thickness and the dimensions of the back reflector grating are given in this work. The grating period structure of the Al back reflector is covered with a graphene layer to improve the absorption of the solar cell, where the periodicity, height, and width of the gratings are optimized. Furthermore, the optimum height of the textured TiO2 layer is simulated to produce the maximum efficiency using light absorption and short circuit current density. In addition, plasmonic nanoparticles are distributed on the textured surface to enhance the light absorption, with different radii, with radius 50, 75, 100, and 125 nm. The absorbed light energy for different nanoparticle materials, Au, Ag, Al, and Cu, are simulated and compared to determine the best performance. The obtained short circuit current density is 61.9 ma/cm2, open-circuit voltage is 0.6 V, fill factor is 0.83, and the power conversion efficiency is 30.6%. The proposed crystalline silicon solar cell improves the short circuit current density by almost 89% and the power conversion efficiency by almost 34%.

Anisotropic temperature-dependent elastic constants and thermal conductivities of TRISO particle coatings

Tristructural isotropic (TRISO) particles show great promise as a candidate fuel for use in several next-generation high-temperature nuclear reactor designs due to their structural integrity and fuel performance at high temperatures and burnups. Computational codes exist that can simulate TRISO fuel performance characteristics and failure probabilities under extreme conditions which require knowledge of the TRISO coatings’ thermophysical properties. The thermophysical descriptions of the TRISO particle's layers (i.e., buffer, pyrolytic carbon, and silicon carbide) currently used in fuel performance codes, however, assume that many of these properties are constant with respect to temperature or texture. In this paper, we obtain the full elastic stiffness tensors of the carbon and silicon carbide layers, which have transversely isotropic symmetry. The calculated elastic properties of each of the coating layers are in remarkable agreement with the current models at room temperature and correct orientations. Additionally, the textured 3C-SiC layer was found to exhibit novel auxetic behavior above 1500 °C. The anisotropic temperature-dependent thermal conductivities of the carbon layers were calculated using acoustical Grüneisen-Debye theory which are in excellent agreement with current models at room temperature and correct orientations. These texture- and temperature-dependent relationships can be incorporated into the thermophysical description of TRISO particles in order to more accurately model fuel performance and failure probabilities under extreme conditions in forthcoming high-fidelity computational simulations.

Lucas Debargue’s performing interpretation as a new horizon for understanding the artistic space of N. Medtner’s Sonata f­moll, op. 5

The relevance. The development of piano performance is the result of the interaction of two alternatives — tradition and innovation. Innovations emerge and are affirmed, overcoming the conservative resistance of traditional performance stamps. Each new stage of this evolutionary process is marked by the appearance on the musical horizon of bright names of pianists, who, stunning the audience with their unexpected performance decisions, break established traditions and form new landmarks for performing and perceiving musical works. In the modern space of world piano performance, such a unique musician is the French pianist Luc Debarg. In his extraordinary interpretations, the pianist convincingly demonstrates the new organics of performance and reflects the latest pianistic trends, which deserves special attention and determines the relevance of the study.
 The purpose of the article consists in realizing the performing conception of Lucas Debargue regarding the N. Medtner’s Sonata f-moll, op. 5 through identification of performing stylistics features of the French pianist.
 The topicality of the article. The research material was: the musical text of the Sonata f-moll, op. 5 M. Medtner; videorecording of the performance of the Sonata by Luc Debargue and Boris Berezovsky.
 The methodology. The study used a historical approach, the method of comparative analysis, musicological and performing analysis
 The result. In musical performance and musicology, N. Medtner’s Sonata f-moll, Op. 5 is grasped as retrospective in the nature of imagery. It is characterized usually by a deep but restrained melancholy, nobility, perfection of form, density and graphic texture, lack of colorism.
 Lucas Debargue sees the Sonata as a symbol of cultural memory. The texture becomes transparent and differentiated, each of the texture layers seems to be visually filled with color and acquires semantic meaning. Comparing textured layers during performance forms an idea of “traveling” through different time spaces. Strengthening the texture and semantic allusions of M. Medtner’s text, L. Debargue, using well-established, historically characteristic types of articulation, corresponds to the style of J. S. Bach, D. Scarlatti, R. Schumann, F. Chopin, S. Rachmaninoff, A. Scriabin.
 Through a carefully constructed system of interaction between microagogics, microdynamics, and texture density, the pianist forms a through image — memories that are revived by the memory but do not lose their brightness.
 So, the leading principle of L. Debargue’s performing style is the modernization of texts of past eras. The pianist offers a post-romantic concept of reading N. Medtner’s Sonata, which actualizes the polylogue of musical and historical eras.
 The scientific novelty. For the first time in musicology, the features of L. Debargue’s performing style are studied.
 The practical significance. The results of the work can be applied in the training courses «Piano», «History of piano performance», in further scientific researches.

Infrared and visible image fusion based on relative total variation decomposition

In this study, we propose an infrared and visible image fusion method based on relative total variation decomposition, which can maintain the contrast information and texture information of source images simultaneously. Firstly, the source images are decomposed into structural layers and texture layers according to the relative total variation. The former is mainly the large frame structure and brightness of the source images, and the latter is the texture and noise with small gradient values. Secondly, the fusion weights are separately constructed according to characteristics of the structure and texture layers. The weights of structure layers are obtained according to the image energy to retain the brightness of source images, and the weights of texture layers are calculated according to the saliency map to retain the texture information. Finally, the fused image could be reconstructed according to previously obtained sub-images and weights. We also conduct qualitative and quantitative experiments on public datasets to verify the effectiveness of the proposed method. The results show that the proposed fusion method has leading advantages in maintaining contrast, avoiding edge blurring and reducing noise compared with several more advanced algorithms, and the fusion result is more in line with human vision.

Fabrication of Catalyst Layers with Preferred Mass and Charge Transport Properties through Texture Engineering

Electrode structure strongly influences the charge and mass transport of energy conversion devices such as polymer electrolyte membrane fuel cells. Various studies have been carried out to optimize the nanoscale features and porosity of the electrode catalyst layer. The impact of large-scale texture, i.e., from several to tens of microns, was however much less investigated. In the present study, we demonstrated the fabrication of catalyst layers directly on the polymer electrolyte membrane with controlled texture by the ultrasonic spray coating technique. By controlling the droplet size of the catalyst ink as well as its drying behaviors, textured catalyst layers with enhanced mass or charge transport properties can be reproducibly obtained through the manipulation of micron-scale Marangoni flows. When the Marangoni effects are maximized, a heavily textured catalyst layer populated with large and deep pores exhibiting good mass transport properties is formed and shows a maximum current density of 1.75 A/cm2. With a slower drying rate of the catalyst ink, a much less textured but more loosely packed catalyst layer is obtained, which shows impressive charge transport properties even under low humidified conditions. With the mass transport resistance greatly alleviated, a maximum power density of 0.8 W/cm2 can be reached at a cathode humidity of 43% at 80 °C using a very low cathode Pt loading of 0.079 mgPt/cm2. Such high performance under low-humidity conditions can lead to significant cost reduction in fuel cell system and its operation.

Contact damage tolerance of alumina-based layered ceramics with tailored microstructures.

This work demonstrates how to enhance contact damage resistance of alumina‐based ceramics combining tailored microstructures in a multilayer architecture. The multilayer system designed with textured alumina layers under compressive residual stresses embedded between alumina–zirconia layers was investigated under Hertzian contact loading and compared to the corresponding monolithic reference materials. Critical forces for crack initiation under spherical contact were detected through an acoustic emission system. Damage was assessed by combining cross‐section polishing and ion‐slicing techniques. It was found that a textured microstructure can accommodate the damage below the surface by shear‐driven, quasi‐plastic deformation instead of the classical Hertzian cone cracking observed in equiaxed alumina. In the multilayer system, a combination of both mechanisms, namely Hertzian cone cracking on the top (equiaxed) surface layer and quasi‐plastic deformation within the embedded textured layer, was identified. Further propagation of cone cracks at higher loads was hindered and/or deflected owed to the combined action of the textured microstructure and compressive residual stresses. These findings demonstrate the potential of embedding textured layers as a strategy to enhance the contact damage tolerance in alumina ceramics.

A high-resolution photogrammetric workflow based on focus stacking for the 3D modeling of small Aegean inscriptions

Any attempt of decipherment and language identification of the scripts from the Aegean dating to the second millennium BCE (namely Cretan Hieroglyphic, Linear A, and Cypro-Minoan) has relied, until today, on traditional catalogues of inscriptions, consisting of incomplete or subjective 2D representations, such as photographs and hand-drawn copies, which are not suitable for documenting such three-dimensional writing systems. In contrast, 3D models of the inscribed media allow for an accurate and objective “autopsy” of the entire surface of the inscriptions. In this context, this work presents an efficient, accurate, high-resolution, and high-quality texture photogrammetric workflow based on focus-stacked macro images, designed for the 3D modeling of small Aegean inscriptions, to properly reconstruct their geometry and to enhance the identification of their signs, making their transcription as unbiased as possible. The pipeline we propose also benefits from a pre-processing stage to remove any coloration difference from the images, and a reliable and simple 3D scaling procedure. We tested this workflow on six inscribed artifacts (two in Cretan Hieroglyphic, three in Linear A, one of uncertain affiliation), whose average size ranges approximately from 1 to 3 cm. Our results show that this workflow achieved an accuracy of a few hundredths of mm, comparable to the technical specifications of standard commercial 3D scanners. Moreover, the high 3D density we obtained (corresponding to the edge average length of the 3D model mesh), up to ≈ 30 µm, allowed us to reconstruct even the smallest details of the inscriptions, both in the mesh and in the texture layer of the 3D models.

HID: The Hybrid Image Decomposition Model for MRI and CT Fusion.

Multimodal medical image fusion can combine salient information from different source images of the same part and reduce the redundancy of information. In this paper, an efficient hybrid image decomposition (HID) method is proposed. It combines the advantages of spatial domain and transform domain methods and breaks through the limitations of the algorithms based on single category features. The accurate separation of base layer and texture details is conducive to the better effect of the fusion rules. First, the source anatomical images are decomposed into a series of high frequencies and a low frequency via nonsubsampled shearlet transform (NSST). Second, the low frequency is further decomposed using the designed optimization model based on structural similarity and structure tensor to get an energy texture layer and a base layer. Then, the modified choosing maximum (MCM) is designed to fuse base layers. The sum of modified Laplacian (SML) is used to fuse high frequencies and energy texture layers. Finally, the fused low frequency can be obtained by adding fused energy texture layer and base layer. And the fused image is reconstructed by the inverse NSST. The superiority of the proposed method is verified by amounts of experiments on 50 pairs of magnetic resonance imaging (MRI) images and computed tomography (CT) images and others, and compared with 12 state-of-the-art medical image fusion methods. It is demonstrated that the proposed hybrid decomposition model has a better ability to extract texture information than conventional ones.

Improved mechanical properties and directional heat transfer performance of h-BN matrix multilayer composites with alternately stacked untextured/textured layers

The h-BN matrix multilayer composites with alternately stacked untextured/textured layers were fabricated by hot-pressing from the alternately stacked “fine h-BN powders” layers and “plate-like h-BN powders + 3Y2O3–5Al2O3” layers. During the hot-pressing process, Y2O3 and Al2O3 reacted, forming Y3Al5O12, which provided a liquid phase environment for h-BN plates to be readily rotated and oriented under the action of the uniaxial pressure. The residual compressive stress in textured layers, which was caused by the mismatch of thermal expansion between textured layers and untextured layers, resulted in crack arrest at the first textured layer in the multilayer composite with 49 vol% textured layers, which improved its flexural strength and fracture toughness by 33.1% and 23.4% compared with the textured monolith, respectively. The multilayer composite with 35 vol% textured layers behaved a much better directional heat transfer performance than the textured and untextured monoliths, making it a promising candidate as thermal management devices in electronics.

Pulsed laser deposition – materials that matter

AbstractPulsed laser deposition (PLD) is the thin film method of choice for depositing complex oxide films and highly textured epitaxial layers with unsurpassed crystallinity. Excimer laser power scalability and PLD process upscaling have resulted in the first systems with 300 mm wafer capability now entering the market. From mobile communication to autonomous driving, novel materials available to high‐tech volume manufacturing matter in transitioning into a 5G connected future.

A Generalized Enhancement Framework for Hazy Images With Complex Illumination

Images captured under low-light conditions are generally characterized by poor illumination, low contrast, and nonignorable large amount of noise. In order to improve the visibility in weak illumination scenes, multiple artificial light sources are used, which leads to severe uneven illumination of the scene. The main challenges of dehazing images with complex illumination are to suppress the boosting of unsightly noise when enhancing contrast and avoid overenhancement in bright glow regions. To circumvent problems above, this letter proposes a generalized enhancement framework, which works well not only in uniform light conditions but also in strongly nonuniform illumination low-light scenes. To achieve this, we first decompose the input hazy image into a structure layer containing low-frequency illumination variance and a texture layer containing large amount of high-frequency details. Sequentially, benefit from two derived masks that are intrinsically similar to weight maps, the proposed framework can perform regional adaptive brightness adjustment on the structure layer according to the distribution of light in the input image. Meanwhile, regions of effective details in the texture layer are assigned higher weights, while regions that belong to noise are suppressed. Finally, adding the enhanced texture layer back to the brightened structure layer, visually appealing results are generated. Experimental results on various scenarios demonstrate the superiority of the proposed framework over state-of-the-art methods in terms of both qualitative and quantitative.

Functional and Anatomical Image Fusion Based on Gradient Enhanced Decomposition Model

Medical image fusion technology can synthesize complementary information from multimodal medical images while saving storage costs. Targeting the problems of reduced contrast, blurred details, color distortion, and excessive processing time requirement in fusion algorithms, we propose a new functional and anatomical image fusion algorithm based on a gradient enhanced decomposition model. First, a new gradient-enhanced decomposition (GED) model is proposed. Subsequently, the proposed GED model is expanded into a three-layer decomposition, wherein the input image is decomposed into a basic, local mean energy, and texture layers using alternating direction method of multipliers. Compared with other classic decomposition method, the proposed three-layer decomposition has the advantages of no additional noise and requiring less time while enhanced gradient. Second, we propose targeted fusion rules for each layer. After the basic layer is weighted averaged, the gamma function is used to adjust its brightness. The product of the local l2 norm and largest singular value constitutes the fusion weights of the local mean energy layers. The fusion weights of the texture layers are determined by the product of the local l1 norm and largest singular value. Finally, the fused image is reconstructed. We perform numerous experiments to prove that the proposed fusion algorithm providing clearer edge details and superior colors in a short processing time, and there was a statistically significant difference compare with other classical algorithms.