Detail Layer Research Articles

Edge-Preserving Filtering-Based Dehazing for Remote Sensing Images

Haze in remote sensing images severely degrades image visibility, making it hard to identify different land covers. This work proposes an edge-preserving filtering-based image dehazing method for remote sensing images, mainly consisting of following several steps. First, the original image contaminated with haze is decomposed by a multiscale guided filtering into base layers that contain haze components and detail layers that reflect the spatial details of input. Then, an optimized atmospheric scattering model is performed on the base layers to eliminate haze. Next, adaptive nonlinear mapping is exploited to enhance image details. Finally, the resulting image is reconstructed by combining the dehazed base layers and the refined detail layers. Experiments on several images demonstrate that the proposed method has an outstanding haze removal capability and yields better dehazing performance compared to other dehazing approaches in terms of objective indexes and subjective results.

An Efficient Detailed Layer Model for Prediction of Separator Damage in a Li-Ion Pouch Cell Exposed to Transverse Compression

An Efficient Detailed Layer Model for Prediction of Separator Damage in a Li-Ion Pouch Cell Exposed to Transverse Compression

Археологическая ГИС Северо-Западной Колхиды: инструмент для сохранения и исследования объектов историко-культурного наследия

The territory of Northwestern Colchis, which today includes both the territory of Abkhazia and the territory of Russia (the Greater Sochi region, Krasnodar Krai), in ancient times was the most important contact zone of many peoples, but has been poorly studied archaeologically. Therefore, full-fledged field research and the creation of a specialized GIS of this territory is an urgent problem. Currently, the territory of Northwestern Colchis is a rapidly developing resort region, which threatens the destruction of historical and cultural heritage sites by anthropogenic activities. The existing GIS today includes 1,780 monuments (dolmens, temples, fortresses, settlements, individual locations and burial grounds without architectural remains above the surface) in the form of point objects. But in recent years, with the development of technologies, orthophotoplanes and DEMs have been included in the GIS structure. Shooting from a UAV and including the obtained models in the GIS structure can solve two problems at once: displaying an archaeological monument in the form of a polygonal theme and obtaining detailed layers displaying landscape conditions. This data will significantly expand the capabilities of GIS. The technique of obtaining an orthophotoplan of an archaeological object was worked out during a series of experiments that helped to identify the basic principles for photogrammetric photography in conditions of dense subtropical vegetation. Another important layer in GIS is historical maps. Their analysis, taking into account the analysis of the semantics of toponymy, the analysis of settlement structures in GIS, and analogies with already known monuments, made it possible to localize a number of toponyms with a high degree of probability. In addition to the analysis of historical maps, the settlement structure itself was analyzed, the main clusters were identified, and a predictive model of the missing fortresses of the Pontic Limes was created.

Research of the thermal interface of LED lighting sources by thermal relaxation differential spectrometry (TRDS)

Thermal relaxation differential spectrometry (TRDS) was used to study the thermal parameters of samples with various design features for heat removal - a powerful LED lamp (150 W) used in industrial and street lighting, low power LED lamps (4 W) with filament emitters, as well as SMD emitter. It is shown that the method of thermal relaxation differential spectrometry is effectively applicable to the study of the structure of thermal parameters of both high-power and low-power LED devices. The method is informative and allows to study in the distribution of thermal resistance and heat flux over the volume and layers of the LED device detail. The use of the TRDS method allows the optimization of the thermal design of LED devices to reduce the overheating temperature of their active regions, and, therefore, to reduce the degradation of LED devices.

A region based remote sensing image fusion using anisotropic diffusion process

ABSTRACT The aim of remote sensing image fusion is to merge the high spectral resolution multispectral (MS) image with high spatial resolution panchromatic (PAN) image to get a high spatial resolution MS image with less spectral distortion. The conventional pixel level fusion techniques suffer from the halo effect and gradient reversal. To solve this problem, a new region-based method using anisotropic diffusion (AD) for remote sensing image fusion is investigated. The basic idea is to fuse the ‘Y’ component only (of YCbCr colour space) of the MS image with the PAN image. The base layers and detail layers of the input images obtained using the AD process are segmented using the fuzzy c-means (FCM) algorithm and combined based on their spatial frequency. The fusion experiment uses three data sets. The contributions of this paper are as follows: i) it solves the chromaticity loss problem at the time of fusion, ii) the AD filter with the region-based fusion approach is brought into the context of remote sensing application for the first time, and iii) the edge info in the input images is retained. A qualitative and quantitative comparison is made with classic and recent state-of-the-art methods. The experimental results reveal that the proposed method produces promising fusion results.

Fusion of Infrared and Visible Images Using Fast Global Smoothing Decomposition and Target-Enhanced Parallel Gaussian Fuzzy Logic.

As a powerful technique to merge complementary information of original images, infrared (IR) and visible image fusion approaches are widely used in surveillance, target detecting, tracking, and biological recognition, etc. In this paper, an efficient IR and visible image fusion method is proposed to simultaneously enhance the significant targets/regions in all source images and preserve rich background details in visible images. The multi-scale representation based on the fast global smoother is firstly used to decompose source images into the base and detail layers, aiming to extract the salient structure information and suppress the halos around the edges. Then, a target-enhanced parallel Gaussian fuzzy logic-based fusion rule is proposed to merge the base layers, which can avoid the brightness loss and highlight significant targets/regions. In addition, the visual saliency map-based fusion rule is designed to merge the detail layers with the purpose of obtaining rich details. Finally, the fused image is reconstructed. Extensive experiments are conducted on 21 image pairs and a Nato-camp sequence (32 image pairs) to verify the effectiveness and superiority of the proposed method. Compared with several state-of-the-art methods, experimental results demonstrate that the proposed method can achieve more competitive or superior performances according to both the visual results and objective evaluation.

Numerical and Experimental Study of Five-Layer Non-Symmetrical Paperboard Panel Stiffness.

This paper concerns the analysis of five-layer corrugated paperboard subjected to a four-point bending test. The segment of paperboard was tested to determine the bending stiffness. The investigations were conducted experimentally and numerically. The non-damaging tests of bending were carried out in an elastic range of samples. The detailed layers of paperboard were modelled as an orthotropic material. The simulation of flexure was based on a finite element method using Ansys® software. Several material properties and thicknesses of papers in the samples were taken into account to analyse the influence on general stiffness. Two different discrete models based on two geometries of paperboard were considered in this study to validate the experimental stiffness. The present analysis shows the possibility of numerical modelling to achieve a good correlation with experimental results. Moreover, the results of numerical estimations indicate that modelling of the perfect structure gives a lower bending stiffness and some corrections of geometry should be implemented. The discrepancy in stiffness between both methods ranged from 3.04 to 32.88% depending on the analysed variant.

A novel multi-focus image fusion method based on joint regularization optimization layering and sparse representation

In this paper, we reported a novel Multi-Focus Image Fusion (MFIF) method based on regularization optimization and sparse representation model, which takes full advantages of the imaging characteristics of multi-focus images. First, a multi-source regularization optimization model was proposed to divide source images into a common background layer and respective detail layers jointly. This makes the focused details scattered in different sources are extracted in concert. Then the resulting detail layers are fused in sparse representation domain. To be more task-specific for MFIF, an atom focus degree measurement based fusion rule was proposed to highlight the focused atoms. Finally, the fusion result is reconstructed by combining the fused detail layer and the common background layer. Thorough experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms some state-of-the-art fusion methods.

Multi-Sensor Fusion of Infrared and Visible Images Based on Modified Side Window Filter and Intensity Transformation

For multi-sensor fusion of infrared and visible images, it is difficult to retain the thermal radiation information of the infrared image and the texture information of the visible image in the fused image. To overcome this problem, a novel infrared and visible image fusion method based on a modified side window filter (MSWF) and an intensity transformation is proposed. First, an MSWF with effective edge-preservation ability is developed by adding four additional kernels to better decompose the source images to obtain the base and detail layers. Furthermore, to extract the edge information of the source images, we propose to further decompose the base layers to obtain the low-frequency layers and high-frequency layers (edge information) through the non-subsampled shearlet transform (NSST). Then, an S-shape intensity transformation function (ITF) is proposed to enhance the saliency information and suppress the non-saliency information in the infrared image. In the fusion process, considering the characteristics of the decomposed components, different fusion rules are designed to obtain the fused detail layer and low- and high-frequency layers. Finally, these fused components are reconstructed to obtain the final fusion image. It is experimentally demonstrated that the proposed method is superior to state-of-the-art fusion methods both in terms of subjective evaluation and objective metrics.

Design of nano-scale multilayered nitride hard coatings deposited by arc ion plating process: Microstructural and mechanical characterization

The objective of the present study was to investigate some transition metal nitride to determine the relationship between microstructural evolution and mechanical properties such as hardness and adhesion strength of the hard coating. Results showed that it was possible to form nano-scale multilayers of Ti(CuNiZr)N/AlCr(SiW)N with chemical fluctuation of Ti-rich and Al-rich nitrides. It was believed that these nano-scale multilayers with chemical fluctuation could form during table rotation of the arc ion plating process, causing difference in mean free path of chemical elements from the target. Microstructural investigation confirmed that the multilayer consisted of three layers with nanostructured periodic stripe patterns on the multilayer coating based on detailed individual layers analysis. Furthermore, Ti(CuNiZr)N/AlCr(SiW)N multilayer showed mechanical properties, including hardness of 36.0 ± 3 GPa and adhesion strength represented by the critical load of 93 ± 5 N with the highest H/E and H3/E2 ratios.

Naturalness balance contrast enhancement using adaptive gamma with cumulative histogram and median filtering

In the domain of contrast enhancement, contrast as well as detail retention are critical for human visualization and onward processing by any other computer based application. Contrast enhancement of an image with naturalness and preserved details is a challenge. In this paper, we propose a method using an edge-preserving smoothing operator approach, based on the weighted least squares (WLS) optimization. The detail and base layers are extracted using the WLS and median filters respectively. Base layer is a smooth layer of the image that contains large scale intensity changes and the detail layer captures the smaller scale details in the image. The layers are enhanced with a fine tuning between the rate of enhancement of the base and detail layers. It is vital to manage the level of enhancement in both the extracted layers as any mistune in the rate of enhancement may lead to severe distortion and addition of artifacts. Color correction is applied to the image to provide a higher-resolution improved image. The simulation results over 680 images from different datasets, show that the proposed method outperforms the existing methods in terms of qualitative and quantitative parameters.

Infrared and visible image fusion based on guided hybrid model and generative adversarial network

To retain the detail information and enhance the visibility of the source images, a new and effective image fusion based on a feature decomposition guided hybrid model and GAN is proposed. Firstly, the source images are divided into low-frequency components and high-frequency components by latent low-rank decomposition. Secondly, a multi-scale image enhancement method is used to improve its detail information for the low-frequency component. The low-frequency components are fused to obtain the base layer image by an improved generative adversarial network method. WLS-based fusion rule is used to improve the contrast of an image and get the detail layer image. Finally, the fused image is reconstructed by the linear combination of final base and detail layer images. Experiments are conducted on public datasets and compared with the state-of-the-art image fusion methods. Our experiments show that the proposed method can achieve good scores based on multiple evaluation indices, such as spatial frequency (SF), peak signal-to-noise ratio (PSNR) and pixel feature mutual information (FMI_pixel).

Multimodal medical image fusion using adaptive co-occurrence filter-based decomposition optimization model.

Medical image fusion has developed into an important technology, which can effectively merge the significant information of multiple source images into one image. Fused images with abundant and complementary information are desirable, which contributes to clinical diagnosis and surgical planning. In this article, the concept of the skewness of pixel intensity (SPI) and a novel adaptive co-occurrence filter (ACOF)-based image decomposition optimization model are proposed to improve the quality of fused images. Experimental results demonstrate that the proposed method outperforms 22 state-of-the-art medical image fusion methods in terms of five objective indices and subjective evaluation, and it has higher computational efficiency. First, the concept of SPI is applied to the co-occurrence filter to design ACOF. The initial base layers of source images are obtained using ACOF, which relies on the contents of images rather than fixed scale. Then, the widely used iterative filter framework is replaced with an optimization model to ensure that the base layer and detail layer are sufficiently separated and the image decomposition has higher computational efficiency. The optimization function is constructed based on the characteristics of the ideal base layer. Finally, the fused images are generated by designed fusion rules and linear addition. The code and data can be downloaded at https://github.com/zhunui/acof. Supplementary data are available at Bioinformatics online.

A Phase Field Model of Electrodeposition with the Effect of Double Layer

The growth of dendrites is a central problem in advanced rechargeable lithium-ion batteries. The phase field method has been used to simulate electrodeposition due to its flexibility in handling complex microstructural morphologies such as dendrites. Generally, two types of phase field models are available. The first type employs a detailed description of the double layer that, unfortunately, is very computationally challenging and hinders its application to studies beyond one dimension. The second type neglects the effect of double layer at the Li-electrolyte interface to simulate systems of practical size in more than one dimension. In this work, we propose a phase field model the does not account for detailed double layer structure but includes the effect of the double layer in the free energy, achieving a balance between computational efficiency and accuracy. The model also accounts for the highly anisotropic interfacial energy of the Li-electrolyte interface, thus allowing the dendrites to have sharp corners. The role of the double layer and highly anisotropic interfacial energy on Li metal dendrite growth will be discussed.

Detail enhancement for infrared images based on Relativity of Gaussian-Adaptive Bilateral Filter

Detail enhancement is the key to the display of infrared image. For the infrared image detail enhancement algorithms, it is very important to present a good visual effect for people effectively. A novel algorithm for detail enhancement of infrared images is proposed in this paper. The method is based on the relativity of Gaussian-adaptive bilateral Filter. The algorithm consists of three steps. The first step is to divide the input image into the base layer and the detail layer by the relativity of Gaussian-adaptive bilateral filter. In the second step, the detail layer is multiplied by the proposed weight coefficient, and the base layer is processed by histogram projection. The third step is to combine the detail layer and the base layer processed in the second step and output it to the 8-bit domain display. Compare with other methods, the new algorithm reduces the running time greatly. The experimental results showed that the proposed algorithm improves the contrast of infrared images effectively.

Region-based adaptive single image dehazing, detail enhancement and pre-processing using auto-colour transfer method

A region-based adaptive single image dehazing and detail enhancement for hazy environments are proposed in this paper. In most of the existing methods, the dark regions of a hazy image become over-dark after dehazing. Hence, there is a need to enhance those regions for better visibility. Also, in the existing methods, the entire image is considered at a time for dehazing and the variations in local region characteristics are not considered limiting the performance of these algorithms. The haze effect of an image varies according to the depth or the object distance, i.e., the more the object distance, the more the effect of haze. Hence, there is a need to adaptively dehaze an image according to haze levels that are affecting various regions of the hazy image for better dehazing performance. To overcome the above drawbacks, in this paper, the auto-colour transfer method is proposed to enhance the dark regions of the hazy image as part of pre-processing. Also, an adaptive single image dehazing method is proposed, which classifies various regions of input hazy image as less affected, moderately affected, and more affected by haze; and subsequently dehazes adaptively according to the haze affected regions. The pre-processed hazy image is passed to three dehazing modules (less affected, moderately affected, and more affected by haze). In each block, the pre-processed hazy image is decomposed into base and detail layers by choosing different scale factors w.r.t different modules. Then in these modules, the image dehazing and detail enhancement are performed for base and detail layers, respectively. Finally, after image dehazing and detail enhancement, the recovered images of three blocks are combined based on the respective regions to obtain the final dehazed output. Quantitative and Qualitative image assessment metrics are evaluated for the proposed method using several datasets, which show better results when compared with the existing methods.

Influence of drinking water quality on the formation of corrosion scales in lead-bearing drinking water distribution systems

Lead in drinking water occurs in drinking water distribution systems (DWDS) where lead pipes are used as service lines. Using data gathered from 4 different Canadian cities, we link drinking water quality to composition of corrosion scales obtained on exhumed lead pipes servicing those municipalities. The data presented encompasses a 10-year span and a detailed layer profile analysis of the solids present in lead bearing service lines; where different layers within the corrosion scale formed inside lead pipes are identified and thoroughly characterized. The results obtained clearly show that the corrosion layers in direct contact with drinking water are rich in lead oxides phases and aluminosilicates. In contrast, lead carbonates are the main phases present on corrosion scales in direct contact with the metallic lead pipe. This heterogeneity on phase distribution is correlated to the radial distance from the corrosion scales to the water/solid interphase and water quality servicing those municipalities. Statistical analysis suggests that dissolved Al, Mn, Cu, Ni, and As accumulate on the corrosion scales with preferential accumulation of specific elements heavily dependent on distinct municipality water quality.

Multi-focus image fusion via Joint convolutional analysis and synthesis sparse representation

Recently, various sparse representation methods have been successfully used in multi-focus image fusion. Most of them produce some spatial artifacts and blurring effects because they only consider the image local information due to the patch processing strategy. In order to reduce the spatial artifacts and blurring effects on the edge details and improve the robustness of the multi-focus image fusion, a novel fusion method based on joint convolutional analysis and synthesis (JCAS) sparse representation is presented. The JCAS model, which integrates the analysis sparse representation and the synthesis sparse representation by using convolutional operation, can effectively separate large-scale structures and fine-scale textures of a single image. First, each source image is decomposed into a base layer and a detail layer using the JCAS model. Second, a Laplacian pyramid transform method is used to fuse the base layers, and a weighted regional energy method is used to fuse the detail layers. Finally, the fused image is reconstructed by combining the fused base and detail layers. Experimental results demonstrate that the proposed method can obtain clearer edge details compared with some popular multi-focus image fusion methods, thus exhibiting state-of-the-art performance in terms of both visual quality and objective assessment.

Optimized Content Adaptive Approach for Image Detailed Enhancement to Reduce Color Distortion

Digital imaging has significantly influenced the outcome of research in various disciplines. For example, artificial intelligence and robotics, biometric security, multimedia and image processing, etc. Technically, image processing and the Human Visual System (HVS) relies heavily on image enhancement to improve the content of the image. One of the biggest challenges in image processing is detail enhancement due to halo artefacts and gradient inversion artefacts at edges. It has been used to enhance the visual quality of an image. Most algorithms that used to enhance the detail of an image essentially depend on edge-preserving decomposition techniques. in general, the image consist of two major elements are a base layer and a detail layer, which extracted by edge-preserving decomposition algorithms. The detail layer is enhanced to improve the details of the generated image. we propose in this paper, a new model to preserve the sharp edges and achieve better visual quality than the existing norm-based algorithm to enhance the details of the image. Experiments show that the proposed method reduces the distortion at the edges. It improves the details of the generated image significantly.

Bilayer Markov Random Field Method for Detecting Defects in Patterned Fabric

Existing block-level defect detection method in patterned fabric causes a large number of false detections due to the lack of edge information. To solve this problem, in this paper, we propose a bilayer Markov random field (BMRF) method for inspecting defects in patterned fabric. First, the proposed method reduces samples of the original fabric image to obtain the constraint layer, which can locate the defective block roughly. Second, we interpolate samples into the image to supplement the local information to improve and optimize the imperfect boundary, to obtain a more detailed data layer. Moreover, this paper proposes a new potential function, which considers the differential characteristics of the image blocks in the same layer and the transition probability between different layers. Finally, this paper utilizes a parameter estimation method based on the expectation maximization to solve the parameters of the BMRF method. The proposed BMRF method is evaluated on databases of star-, box- and dot-patterned fabrics. By comparing the resultant and ground-truth images, the recall rate of the proposed method in the three patterned fabrics is 95.32%, 89.29% and 93.28%, respectively, which is comparable to the existing methods.