Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

New algorithm for infrared small target image enhancement based on wavelet transform and human visual properties

The key to the wavelet based denoising teehniquea is how to manipulate the wavelet coefficients. By referring to the idea of Inclusive-OR in the design of circuits, this paper proposes a new algorithm called wavelet domain Inclusive-OR denoising algorithm（WDIDA）, which distinguishes the wavelet coefficients belonging to image or noise by considering their phases and modulus maxima simultaneously. Using this new algorithm, the denoising effects are improved and the computation time is reduced. Furthermore, in order to enhance the edges of the image but not magnify noise, a contrast nonlinear enhancing algorithm is presented according to human visual properties. Compared with traditional enhancing algorithms, the algorithm that we proposed has a better noise reducing performanee , preserving edges and improving the visual quality of images.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

Optimizing Spatial Relationships in GCN to Improve the Classification Accuracy of Remote Sensing Images

Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation.With the continuous develop-ment of artificial intelligence technology,the use of deep learning methods for interpreting remote-sensing images has matured.Existing neural networks disregard the spatial relationship between two targets in remote sensing images.Semantic segmentation models that combine convolutional neural networks(CNNs)and graph convolutional neural networks(GCNs)cause a lack of feature boundaries,which leads to the unsatisfactory segmentation of various target feature boundaries.In this paper,we propose a new semantic segmentation model for remote sensing images(called DGCN hereinafter),which combines deep semantic segmentation networks(DSSN)and GCNs.In the GCN module,a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships.A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship informa-tion in the original feature information.Extensive experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images,the DGCN significantly optimizes the segmentation effect of feature boundaries,effectively reduces the noise in the segmentation results and improves the segmentation accuracy,which demonstrates the advancements of our model.

Two new approaches for image registration based onspatial-temporal relationship

How to improve the probability of registration and precision of localization is a hard problem, which is desiderated to solve. The two basic approaches (normalized cross-correlation and phase correlation) for image registration are analysed, two improved approaches based on spatial-temporal relationship are presented. This method adds the correlation matrix according to the displacements in x- cirection and y- directions, and the registration pose is searched in the added matrix. The method overcomes the shortcoming that the probability of registration decreasing with area increasing owing to geometric distortion, improves the probability and the robustness of registration.

Image Retrieval Based on Objects’ Spatial Orientation Relationships

A new concept of characteristic scanning radial (CSR) is proposed for thesegmented image on the basis of two shape-specific points of its shape-objects. Subsequently, twocharacteristic attribute sequences (CAS) of relative distance and relative direction are derived torepresent the spatial orientation relationships among objects of the image. A novel image retrievalalgorithm is presented using these two CASs. The proposed retrieval approach not only satisfies thetransformational invariance, butalso attains the quantitative comparison of matching. Experimentsidentify the effectiveness and efficiency of the algorithm adequately.

Mother-Daughter Relationship and Daughter’s Body Image

The adolescent years are characterized by emotional upheaval and hormonal and physiological changes that often create tension and conflicts between girls and their parents. This research study is based on an analysis of the mother-adolescent daughter relationship, with 46 mother-daughter dyads. This research assessed the effect of the daughter’s body image (independent variable) and her view of her own mother-daughter relationship (independent variable) on her sense of wellbeing (dependent variable). This study used four questionnaires to evaluate the dyadic model: the Modified Gray’s Questionnaire (Body Image), the Leisure Time Exercise Questionnaire (LTEQ), the Mental Health Inventory (MHI) for measurement of the subjective sense of wellbeing, and the Relationship with Mother Questionnaire. Study findings show the importance of the adolescent girl’s positive body image on her sense of wellbeing, as well as the centrality of the mother-daughter relationship in the daughter’s body image and wellbeing.

Study on threshold segmentation of multi-resolution 3D human brain CT image

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

Human Body Image Edge Detection Based on Wavelet Transform

Human dresses are different in thousands way. Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method or gray method to detect human body image edges can't obtain satisfactory results because of false detections and missed detections. According to the peculiarity of human body image, dyadic wavelet transform of cubic spline is successfully applied to detect the face and profile edges of human body image and Mallat algorithm is used in the wavelet decomposition in this paper.

Research on the Video Detection of Human Respiratory Motion Based on Sequential Images

Objective:Explore the feasibility of the high precision accelerometer for measuring the human respiratory displacement.Methods:A wireless acceleration acquisition system with the low power consumption and the high precision was designed with the high precision acceleration sensor ADXL355 as the core device.Based on the frequency characteristics of the breathing motion and the principle that the displacement can be calculated by the acceleration quadratic integration,two displacement measurement algorithms for the quasi-periodic weak motion are designed.Results:The simulation results show that the proposed algorithm is effective.The experimental results show that the designed acquisition system and algorithm can calculate the human respiratory displacement.Conclusion:The high precision accelerometer can be used to measure the human respiratory displacement,which provides a new method for the measurement of the human respiratory displacement.

The Image and Text Relationship in TANG Yin＇s Scroll of Poetry and Painting

This paper seeks to examine the image and text relationship in TANG Yin＇s scroll of poetry and painting from three aspects： The first aspect focuses upon the schema type of its image and text relationship in physical form; the second aspect, explores the text＇s/poetry＇s functions of anchorage and relay while appreciating those images/paintings; the third aspect, traces the semiosis process of image, exploring how image and text as cultural products in the epistemological world mediates with the phenomenological world

Relationship Between Tyndall Light Path Attenuation and Concentration Based on Digital Image

When the red laser illuminates the lyosol, the Tyndall effect will form a light path with a certain distance, and the optical properties of the lyosol will have a certain influence on the Tyndall light intensity. This paper mainly aims at the theoretical and experimental studies on the change situation of the lyosol concentration and the attenuation characteristics of the light path when the red laser changes with the distance of the light path in the solution. In order to study the effect of lyosol concentration on the Tyndall light path, digital image technology was applied to the measurement of lyosol concentration. Due to the non-contact property of the image, the liquid concentration can be measured accurately in real time. The attenuation characteristics of the laser in the lyosol were obtained by image processing technology, and the quantitative relationship between the attenuation coefficient of the Tyndall light path and the lyosol concentration was obtained.

SAR Image Quality Assessment System Based on Human Visual Perception for Aircraft Electromagnetic Countermeasures

In electronic confrontation, Synthetic Aperture Radar (SAR) is vulnerable to different types of electronic jamming. The research on SAR jamming image quality assessment can provide the prerequisite for SAR jamming and anti-jamming technology, which is an urgent problem that researchers need to solve. Traditional SAR image quality assessment metrics analyze statistical error between the reference image and the jamming image only in the pixel domain; therefore, they cannot reflect the visual perceptual property of SAR jamming images effectively. In this demo, we develop a SAR image quality assessment system based on human visual perception for the application of aircraft electromagnetic countermeasures simulation platform.The internet of things and cloud computing techniques of big data are applied to our system. In the demonstration, we will present the assessment result interface of the SAR image quality assessment system.

Design and Implementation of the Image Interactive System Based on Human-Computer Interaction

Based on the traditional Human-Computer Interaction method which is mainly touch input system, the way of capturing the movement of people by using cameras is proposed. This is a convenient technique which can provide users more experience. In the article, a new way of detecting moving things is given on the basis of development of the image processing technique. The system architecture decides that the communication should be used between two different applications. After considered, named pipe is selected from many ways of communication to make sure that video is keeping in step with the movement from the analysis of the people moving. According to a large amount of data and principal knowledge, thinking of the need of actual project, a detailed system design and realization is finished. The system consists of three important modules: detecting of the people's movement, information transition between applications and video showing in step with people's movement. The article introduces the idea of each module and technique.

Construction of three-dimensional atlas of the lenticular nuclei and its subnucleus based on the cryosection images from Chinese visible human:a preliminary study

Objective To establish a 3D atlas of the lenticular nuclei and its subnucleus with the cryosection images of the male from "Atlas of Chinese Visible Human". Methods The lenticular nuclei and its subnucleus were segmented from the cryosection images and reconstructed with the software

Industrial Dehumanization——Viewed from the husband and wife relationship in "Odour of Chrysanthemums"

Lawrence is regarded as one of most accomplished short story writers in twentieth century,with "Odour of Chrysanthemums" one of his early works.Through the death of a miner,the text shows how humanity was ruined by industrial civilization.This essay is intended to unveil the destructive force by analyzing the relationship between the husband and wife.

Magnetic resonance diffusion tensor imaging and diffusion tensor tractography of human visual pathway

AIM: To investigate the visual pathway in normal subjects and patients with lesion involved by diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT). METHODS: Thirty normal volunteers, 3 subjects with orbital tumors involved the optic nerve (ON) and 33 subjects with occipital lobe tumors involved the optic radiation (OR) (10 gliomas, 6 meningiomas and 17 cerebral metastases) undertook routine cranium magnetic resonance imaging (MRI), DTI and DTT. Visual pathway fibers were analyzed by DTI and DTT images. Test fractional anisotropy (FA) and mean diffusivity (MD) values in different part of the visual pathway. RESULTS: The whole visual pathway but optic chiasm manifested as hyperintensity in FA maps and homogenous green signal in the direction encoded color maps. The optic chiasm did not display clearly. There was no significant difference between the bilateral FA values and MD values of normal visual pathway but optic chiasm, which the FA values tested were much too low (all P>0.05). The ONs of subjects with orbital tumors were compressed and displaced. Only one subject had lower FA values and higher MD values. OR of 9 gliomas subjects were infiltrated, with displacement in 2 and disruption in 7 subjects. All OR in 6 meniongiomas subjects were displaced. OR in 17 cerebral metastases subjects all developed displacement while 7 of them had disruption also. CONCLUSION: MR-DTI is highly sensitive in manifesting visual pathway. Visual pathway can be analyzed quantitatively in FA and MD values. DTT supplies accurate three dimensional conformations of visual pathway. But optic chiasm's manifestation still needs to improve.

Remodeling of motor cortex function in acute cerebral infarction patients following human urinary kallidinogenase A functional magnetic resonance imaging evaluation after 6 months

A total of 29 patients were treated within 48 hours after acute subcortical cerebral infarction with Xuesaitong or Xuesaitong plus human urinary kallidinogenase for 14 days. Neurological deficits, activity of daily living, and evaluations of distal upper limb motor functions at the 6-month follow-up showed that patients treated with Xuesaitong plus human urinary kallidinogenase recovered better than with Xuesaitong alone. In addition, functional MRI revealed that activation sites were primarily at the ipsilesional side of injury in all patients. Human urinary kallidinogenase induced hyperactivation of the ipsilesional primary sensorimotor cortex, premotor cortex, supplementary motor area, and contralesional posterior parietal cortex. Results showed that human urinary kallidinogenase improved symptoms of neurological deficiency by enhancing remodeling of long-term cortical motor function in patients with acute cerebral infarction.

In vivo tracking of human adipose-derived stem cells labeled with ferumoxytol in rats with middle cerebral artery occlusion by magnetic resonance imaging

Ferumoxytol, an iron replacement product, is a new type of superparamagnetic iron oxide ap- proved by the US Food and Drug Administration. Herein, we assessed the feasibility of tracking transplanted human adipose-derived stem cells labeled with ferumoxytol in middle cerebral artery occlusion-injured rats by 3.0 T MRI in vivo. 1 × 104 human adipose-derived stem cells labeled with ferumoxytol-heparin-protamine were transplanted into the brains of rats with middle cerebral artery occlusion. Neurologic impairment was scored at 1, 7, 14, and 28 days after transplantation. T2-weighted imaging and enhanced susceptibility-weighted angiography were used to observe transplanted cells. Results of imaging tests were compared with results of Prussian blue staining. The modified neurologic impairment scores were significantly lower in rats transplanted with cells at all time points except I day post-transplantation compared with rats without transplantation. Regions with hypointense signals on T2-weighted and enhanced susceptibility-weighted angiography images corresponded with areas stained by Prussian blue, suggesting the presence of superparamagnetic iron oxide particles within the engrafted cells. Enhanced susceptibility-weighted angiography image exhibited better sensitivity and contrast in tracing ferumoxytol-heparin-protamine-labeled human adipose-derived stem ceils compared with T2-weighted imaging in routine MRI.

Objective measurement for image defogging algorithms

Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.