I-DCGAN and TOPSIS-IFP:A simulation generation model for radiographic flaw detection images in light alloy castings and an algorithm for quality evaluation of generated images

The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.

Non-invasive evaluation of liver steatosis with imaging modalities:New techniques and applications

In the world,nonalcoholic fatty liver disease(NAFLD)accounts for majority of diffuse hepatic diseases.Notably,substantial liver fat accumulation can trigger and accelerate hepatic fibrosis,thus contributing to disease progression.Moreover,the presence of NAFLD not only puts adverse influences for liver but is also associated with an increased risk of type 2 diabetes and cardiovascular diseases.Therefore,early detection and quantified measurement of hepatic fat content are of great importance.Liver biopsy is currently the most accurate method for the evaluation of hepatic steatosis.However,liver biopsy has several limitations,namely,its invasiveness,sampling error,high cost and moderate intraobserver and interobserver reproducibility.Recently,various quantitative imaging techniques have been developed for the diagnosis and quantified measurement of hepatic fat content,including ultrasound-or magnetic resonancebased methods.These quantitative imaging techniques can provide objective continuous metrics associated with liver fat content and be recorded for comparison when patients receive check-ups to evaluate changes in liver fat content,which is useful for longitudinal follow-up.In this review,we introduce several imaging techniques and describe their diagnostic performance for the diagnosis and quantified measurement of hepatic fat content.

DAMAGE AND FRACTURE EVALUATION OF GRANULAR COMPOSITE MATERIALS BY DIGITAL IMAGE CORRELATION METHOD

This paper presents the applications of digital image correlation technique to the mesoscopic damage and fracture study of some granular based composite materials including steel- fiber reinforced concrete,sandstone and crystal-polymer composite.The deformation fields of the composite materials resulted from stress localization were obtained by the correlation computation of the surface images with loading steps and thus the related damage prediction and fracture parameters were evaluated.The correlation searching could be performed either directly based on the gray levels of the digital images or from the wavelet transform(WT)coefficients of the transform spectrum.The latter was developed by the authors and showed higher resolution and sensitivity to the singularity detection. Because the displacement components came from the rough surfaces of the composite materials without any coats of gratings or fringes of optical interferometry,both surface profiles and the deformation fields of the composites were visualized which was helpful to compare each other to analyze the damage of those heterogeneous materials.

Extraction of auroral oval boundaries from UVI images: A new FLICM clustering-based method and its evaluation

Based on the fuzzy local information c-means （FLICM） clustering algorithm, a new method is developed for extracting the equatorward and poleward boundaries of the auroral oval from images acquired by the Ultraviolet Imager （UVI） aboard the POLAR satellite. First, the method iteratively segments the UVI image with the FLICM clustering algorithm, according to an integrity criterion for the segmented auroral oval. Then, possible gaps in the extracted auroral oval are filled, based on prior knowledge of its shape. To evaluate the method objectively, the extracted boundaries are compared with the precipitating electron boundaries determined from DMSP satellite precipitation particle data. The evaluation results demonstrate that the proposed method generates more accurate auroral boundaries than traditional methods.

Objective Fitting Evaluation Model for Dressing Fit Based on Wrinkle Index of Dressing Image

An effective model(image to wrinkle, ITW) for garment fitting evaluation is presented. The proposed model is to improve the accuracy of garment fitting evaluation based on dressing image. The ITW model is an objective evaluation model of fitting based on the wrinkle index of dressing image. The ITW model consists of two main steps, the gray curve-fitting(GCF) threshold segmentation algorithm and Canny edge detection algorithm. In the ITW model, three types of wrinkle trends are defined. And the network dressing image is evaluated and simulated by three quantitative indexes: wrinkle number, wrinkle regularity and wrinkle unevenness. Finally, the fitness of three kinds of dress effects(tight, fit and loose) is quantified by objective fitting evaluation model.

Comprehensive Evaluation of Modern Enterprise Image with Theory of Fuzzy Mathematics

The multi layered indexes for modern enterprise images are summarized to overcome the shortness of single layered ones in evaluating modern enterprise images. The relevant multi layered evaluation model and algorithm are proposed. As an example, an electrical appliance enterprise in Qingdao city is evaluated with the proposed model and evaluation indexes to show the reasonability of the proposed indexes.

Synthetically Evaluation System for Multi-source Image Fusion and Experimental Analysis

Study on the evaluation system for multi-source image fusion is an important and necessary part of image fusion. Qualitative evaluation indexes and quantitative evaluation indexes were studied. A series of new concepts, such as independent single evaluation index, union single evaluation index, synthetic evaluation index were proposed. Based on these concepts, synthetic evaluation system for digital image fusion was formed. The experiments with the wavelet fusion method, which was applied to fuse the multi-spectral image and panchromatic remote sensing image, the IR image and visible image, the CT and MRI image, and the multi-focus images show that it is an objective, uniform and effective quantitative method for image fusion evaluation.

OBJECTIVE IMAGE QUALITY MEASURES AND THEIR APPLICATIONS IN SEGMENTATION EVALUATION

Image segmentation is a critical step of image analysis. Segmentation evaluation is an effective procedure for studying the performance of segmentation techniques, in which quality measure plays an important role. This paper presents a group of new objective quality measures for segmentation evaluation and compares their performances. In addition, to verify the effectiveness of these new measures, an appropriate classification of segmentation is proposed. According to this classification, several representative algorithms from different categories are selected for comparison testing. Some valuable results are obtained and presented.

Image Zernike Moments Shape Feature Evaluation Based on Image Reconstruction

影像特征描述符的精确性的评估途径在影像特征抽取起一个重要作用。我们指出图象形状特征能被当简短介绍 Zernike 时刻的基本概念时，时刻设置了的 Zernike 描述。在基于 Zernike 时刻的反的转变谈论图象重建技术以后， Zernike 时刻的精确性的评估途径经由在原来的图象之间的不同度和重建比率塑造特征，重建的图象被建议。实验结果证明图象 Zernike 时刻的这条评估途径的可行性塑造特征。

Perceptual Quality Assessment of Omnidirectional Images:Subjective Experiment and Objective Model Evaluation

Virtual reality(VR) environment can provide immersive experience to viewers.Under the VR environment, providing a good quality of experience is extremely important.Therefore, in this paper, we present an image quality assessment(IQA) study on omnidirectional images. We first build an omnidirectional IQA(OIQA) database, including 16 source images with their corresponding 320 distorted images. We add four commonly encountered distortions. These distortions are JPEG compression, JPEG2000 compression, Gaussian blur, and Gaussian noise. Then we conduct a subjective quality evaluation study in the VR environment based on the OIQA database. Considering that visual attention is more important in VR environment, head and eye movement data are also tracked and collected during the quality rating experiments. The 16 raw and their corresponding distorted images,subjective quality assessment scores, and the head-orientation data and eye-gaze data together constitute the OIQA database. Based on the OIQA database, we test some state-of-the-art full-reference IQA(FR-IQA) measures on equirectangular format or cubic formatomnidirectional images. The results show that applying FR-IQA metrics on cubic format omnidirectional images could improve their performance. The performance of some FR-IQA metrics combining the saliency weight of three different types are also tested based on our database. Some new phenomena different from traditional IQA are observed.

Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set

In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.

Non-reference virtual view quality evaluation of MVD

The quality of virtual view based on multi-view video （MVD） plus depth format is often evaluated by PSNR or subjectively judged. However, due to synthesizing arbitrary view images, the virtual view images mostly have no reference images and are only assessed using non-reference. Virtual view images synthesized by depth estimation reference software （DERS） and view synthesis reference software （VSRS） often accompanied with blockiness and other distortions on the edge. In addition, matching level for the depth map and the corresponding texture maps of left and right views also affects the quality of the virtual view. This paper compares the edge similarity of the depth and the corresponding texture maps which generate the intermediate virtual view and combined with the virtual view＇s blockiness which causing blur to evaluate the quality of the virtual view. Experiment results show that the proposed method can reflect the quality of the virtual view better.

Analysis of the Relevance of Evaluation Criteria for Multicomponent Image Segmentation

Image segmentation is an important stage in many applications such as image, video and computer processing. Generally image interpretation depends on it. The materials and methods used to demonstrate are described. The results are presented and analyzed. Several approaches and algorithms for image segmentation have been developed, but it is difficult to evaluate the efficiency and to make an objective comparison of different segmentation methods. This general problem has been addressed for the evaluation of a segmentation result and the results are available in the literature. In this work, we first presented some criteria of evaluation of segmentation commonly used in image processing with reviews of their models. Then multicomponent synthetic images of known composition are applied to these criteria to explore the operation and evaluate its relevance. The results show that choosing an assessment method depends on the purpose, however the criterion of Zeboudj appears powerful for the evaluation of region segmentations for properly separated classes, on the contrary the criteria of Levine-Nazif and Borsotti are adapted to the methods of classification and permit to build homogeneous regions or classes. The values of the Rosenbeger criterion are generally low and similar, so hard to make a comparison of segmentations with this criterion.

Image Perception and Emotional Evaluation of Zhuhai Coastal Tourism Destination Based on Network Big Data Analysis

By capturing the online evaluation data of tourists on Zhuhai coastal tourist attractions in the two domestic OTA platforms Ctrip and Mafengwo, word cloud and word frequency statistics and complex semantic network analysis are used to carry out the research of image perception and emotional evaluation. The results show that:① Zhuhai City is the main body of the composition of tourists' image perception of Zhuhai coastal tourism, showing the 3S(seafood, sand, scenery) coastal tourism image of Zhuhai characteristics, and the rapid development of Zhuhai City is an important driving force for the transformation of the city's image perception.② The single core and two wings is the hierarchical pattern of tourists' image perception of Zhuhai's coastal tourism, namely Zhuhai as the centre and sand and seafood as the two wings.③ After evaluating the emotions of tourists in Zhuhai's coastal area, it is found that overall the tourists' perceived image is relatively good, with a large proportion of positive emotions, but there are also some negative and neutral emotions.

PERFORMANCE EVALUATION OF SEVERAL FUSION APPROACHES FOR CCD/SAR IMAGES

Several image fusion approaches for CCD/SAR images are studied and the performance evaluation of these fusion approaches is completed in this paper. Firstly, the preprocessing of CCD/SAR images before fusion is fulfilled. Then, the image fusion methods including linear superposition, nonlinear operator method and multiresolution methods, of which the multiresolution methods include Laplacian pyramid, ratio pyramid, contrast pyramid, gradient pyramid, morphological pyramid and discrete wavelet transform, are adopted to fuse two types of images. Lastly, the four performance measures, standard deviation, entropy, cross entropy and spatial frequency, are calculated to compare the fusion results by different fusion approaches in this paper. Experimental results show that contrast pyramid, morphology pyramid and discrete wavelet transformation in multiresolution approaches are more suitable for CCD/SAR image fusion than other ones proposed in this paper and the objective performance evaluation of CCD/SAR image fusion approaches are effective.

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy.Deep learning provides a high performance for several medical image analysis applications.This paper proposes a deep learning model for the medical image fusion process.This model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR images.Then,an additional process is executed on the extracted features.After that,the fused feature map is reconstructed to obtain the resulting fused image.Finally,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality.Different realistic datasets of different modalities and diseases are tested and implemented.Also,real datasets are tested in the simulation analysis.

Performances of conventional fusion methods evaluated for inland water body observation using GF-1 image

Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening(PS), principal component analysis(PCA), Gram-Schmidt(GS), and wavelet fusion(WF), are utilized to fuse MS and PAN images of GF-1.The results of subjective and objective evaluation methods application indicate that GS performs the best,followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.

Susceptibility weighted imaging in the evaluation of hemorrhagic diffuse axonal injury

Diffuse axonal injury（DAI）is axonal and small vessel injury produced by a sudden acceleration of the head by an external force,and is a major cause of death and severe disability（Paterakis et al.,2000）.Prognosis is poorer in patients with apparent hemorrhage than in those without（Paterakis et al.,2000）.Therefore,it is important to identify the presence and precise position of hemorrhagic foci for a more accurate diagnosis.CT and magnetic resonance imaging（MRI）have long been applied in the diagnosis of DAI, but they are not sensitive enough for the detection of small hemorrhagic foci, and cannot meet the requirements for early diagnosis. A major advance in MRI has been the development of susceptibility weighted imaging （SWI）, which has greatly increased the ability to detect small hemorrhagic foci after DAI （Ashwal et al., 2006）.

Flow control performance evaluation of a tri-electrode sliding discharge plasma actuator

Tri-electrode sliding discharge(TED)plasma actuators are formed by adding a direct current(DC)exposed electrode to conventional dielectric barrier discharge(DBD)plasma actuators.There are three TED modes depending on the polarity and amplitude of the DC supply:DBD discharge,extended discharge and sliding discharge.This paper evaluates the electrical,aerodynamic and mechanical characteristics of a TED plasma actuator based on energy analysis,particle image velocimetry experiments and calculations using the Navier-Stokes equation.The flow control performances of different discharge modes are quantitatively analyzed based on characteristic parameters.The results show that flow control performance in both extended discharge and sliding discharge is more significant than that of DBD,mainly because of the significantly higher(up to 141%)body force of TED compared with DBD.However,conductivity loss is the primary power loss caused by the DC polarity for TED discharge.Therefore,power consumption can be reduced by optimizing the dielectric material and thickness,thus improving the flow control performance of plasma actuators.