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Fast algorithms of image Fusion for Super-Resolution Reconstruction from Multiple images with Random Shifts

Chinese Journal of Electronics 2023年第1期21卷 69-72页

作者： Beijia Ning Jie Li xinbo Gao VIPS Laboratory School of Electronic Engineering Xidian University Xi'an China

The commonly-used image Super-resolution (SR) reconstruction methods are computationally expensive and only applicable to “offline processing” cases. To reduce the work-load of SR fusion, three algorithms, i.e., Neighborhood searching nearest interpolation (NSNI), Fixed-distance searching nearest interpolation (FDSNI) and Two-direction linear interpolation (TDLI), are proposed in this paper in the condition of multiple lower resolution images with global random shifts. The simulation results illustrate the effectiveness, speed and quality advantages of the three proposed SR algorithms over classic algorithms.

关键词： Interpolation Simulation Superresolution Reconstruction algorithms Real-time systems image fusion image reconstruction

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An Overview of Different Approaches used for processing Satellite images

An Overview of Different Approaches used for Processing Sate...

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2024 International conference on Distributed systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Chethana, V. Rashmi, S. Visvesveraya Technological University Dayananda Sagar College of Engineering Department of Information Science & Engineering Belagavi India Visvesveraya Technological University Dayananda Sagar College of Engineering Department of Computer Science & Engineering (Data Science) Belagavi India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In country such as India due to monsoon and frequent climatic changes leads to natural disasters such as floods, drought, landslides, cyclones, forest fire, earthquake and so on. The post disaster badly impacts on human life, economy and environment. As remote sensing technology advanced, the satellite images with high resolution can be processed and analyzed to get the required results. These analyzed results can be used for rescue operations after disaster or to predict any disasters before their occurrences. Some of the applications such as post disaster event management widely use onboard processing to get faster analyzed results. Various operations such as satellite image- enhancement, compression, classification and segmentation are performed on satellite images. Results of these techniques mainly used in applications such as disaster management, Land Use Land Cover, Change detection, landslide and many more. In recent days, satellite image processing are performed using new technical aspects such as deep learning, Artificial intelligence and machine learning to get fast and more accurate results. Edge-Computing-Enabled Deep Learning and Leveraging Deep Learning are the approaches used for satellite image enhancement. Panoptic Segmentation of Multispectral Satellite images approaches are mainly used for satellite image segmentation. Deep Learning based approach and Genererative Adverserial Network architecture are applied to get change detection in satellite images. Lossless algorithm for hyperspectral data compression and Neural network lossy compression techniques are preferably used for satellite image compression. Approaches such as Hybrid data model centric approach and Multiscale Convolutional Neural Network are applied to perform object detection. And also machine learning algorithms are mainly used for classification of satellite images. This paper provides comparative study survey on these various approaches used for processing of satellite images. ©

关键词： convolutional neural network deep learning disaster remote sensing satellite images

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Nature-Inspired algorithms for image Enhancement

Nature-Inspired Algorithms for Image Enhancement

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IEEE International Midwest Symposium on Circuits and systems (MWSCAS)

作者： Dhruve, Keyuri Kaur, Devinder Univ Toledo EECS Dept Toledo OH 43606 USA

ISBN: (纸本)9781665424615

The paper presents a comparative analysis of nature inspired Artificial Bee Colony and Particle Swarm Optimization algorithms for the enhancement of images. The paper briefly reviews the swarm-based algorithms. It describes the dynamics of ABC and Particle Swarm Optimization algorithm and mapping for image enhancement. The key purpose of image processing is to extract essential features from images. During an image processing operation, the input given is an image, and its output is an enhanced high-quality image based on some characteristics/features related to that image. The mapping of the ABC and PSO algorithms for image enhancement was implemented using the Matlab image processing toolbox. For both algorithms ABC and PSO, the parameters of Contrast Enhancement alpha was set in the range of 0-255 and beta was set in the range of 1-109. Finally, the Comparative Analysis of ABC and PSO algorithms was done using the metrics of Entropy, MSE and PSNR values for the images. It is found that ABC yields a better performance as compared to PSO.

关键词： component formatting style styling insert

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A Comparison on Dental X-ray Segmentation based on Fuzzy Clustering for ADIS System

A Comparison on Dental X-ray Segmentation based on Fuzzy Clu...

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2022 International conference on Computing, Communication, Security and Intelligent systems, IC3SIS 2022

作者： Joy, Manoj T. Shan, B. Priestly Shaji, Liyan Grace Sathyabama Institute of Science Technology Computer Science Engineering Chennai India Chandigarh University Mohali Dean Academic Affairs Punjab India Amal Jyothi College of Engineering Kanjirappally India

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

image segmentation in image processing is the method by which images are divided into different segments. Many methods are available and one of the commonly used method are fuzzy-based systems. This paper ignites light into different fuzzy clustering methods, used for the segmentation of dental radiographs. Fuzzy C-means (FCM), Semi-Supervised fuzzy clustering with spatial constraints (SSFC-SC) and Fuzzy Level Set Algorithm (SFC-LM) are discussed in this paper for the automated dental radiograph segmentation. The three algorithms give different performance rate based on time and accuracy. These algorithms are evaluated against a real dental radiograph dataset available, and this set contains panoramic and periapical radiographs. The proposed study help in identifying the strengths and weakness of the algorithms. From the comparisons, it is inferred that the SSFC-SC based systems fair better than its counterparts. © 2022 IEEE.

关键词： image segmentation

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image Forgery Detection Using Deep Learning Framework 5

Image Forgery Detection Using Deep Learning Framework

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5th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2022

作者： Deng, Yuchao School of Software Northwestern Polytechnical University Xi'an China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

With the continuous development of digital media and the rapid change of digital image processing equipment, digital images are easy to be tampered with by criminals. In recent years, illegal elements often use tampering images to commit crimes, so it is urgent to study image forensics technology. Blind forensics technology is used to verify the original and authenticity of the image, and then the tampering mode of the image is discriminated and the tampered area is located according to the tampering trace of the image. This paper focuses on the detection of image manipulation and tampering and the localization of tampered areas. The main innovation of this paper is to use the core idea of end-to-end training of u-net network to further optimize the performance of image tampering detection tasks in combination with residual network. © 2022 IEEE.

关键词： image classification

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Score Level Fusion Based on T-Norms Algorithm

Score Level Fusion Based on T-Norms Algorithm

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International conference on image and Signal processing and their Applications (ISPA)

作者： Hellal Leila Naceur-Eddine Boukezzoula Mohamed Cheniti LTS Laboratory University Ferhat Abbas Setif Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Multimodal biometric systems unite data from multiple biometric sources to surmount the limitations of individual systems. This paper presents an efficient algorithm for extracting details based on the image representation of fingerprints. Additionally, we conduct an in-depth investigation of T-Norm algorithms and offer a comprehensive score-level merging analysis. This analysis combines matching outcomes derived from the left and right fingerprints using the Hammacher, Schweizer-Sklar, Dombi, and Yager algorithms. The latter approaches are compared based on the receiver operating characteristic curve. The experimental evaluation conducted on the NIST fingerprint database validates the efficacy of fusion at the score level. Furthermore, it demonstrates the effectiveness of the proposed algorithm when compared to other T-Norms.

关键词： Databases Merging Signal processing algorithms Receivers Fingerprint recognition Signal processing NIST

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A comparative analysis of techniques for extracting features from the object in image processing 5

A comparative analysis of techniques for extracting features...

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5th IEEE International conference on Advanced systems and Emergent Technologies (IC_ASET)

作者： Hafsia, Touka Tlijani, Hatem Nouri, Khaled Polytechn Sch Tunisia Adv Syst Lab BP 743 La Marsa Tunisia Natl Engn Sch Tunis Automat Res Lab BP 37 El Manar campus Tunis Tunisia

ISBN: (纸本)9781665427357

Visual informations is very rich to control and pursue mobile robots, like wheeled mobile robots, underwater robots and aerospace mobile robots, etc. These are considered as mobile robots that move in different spaces, their main problem in this case is navigation especially in unknown environments. In effect, this navigation is possible only by the localisation and orientation of the robot by using different embedded sensors. We have the camera which is a necessary sensor in this work, it is an embedded instrument that gives very rich visual information as a sensor complementing the other sensors. In this context, the recognition of objects from visual informations is a main function among the functions very useful in image processing tasks due to its varied applications in the field of robotics. Based on the analysis of this informations and the determination of image features like color or shape or object primitives (points, lines, edges, etc.) or some other features. What interest us in this paper, various feature extraction techniques and classification of point and edge detection are discussed which are required for object recognition showing advantages and disadvantages of the selected algorithms. So, points and edge detection refers to the process of identifying and detecting sharp discontinuities in an image. In this work, we try to develop a novel algorithm using the work on the existing to create a novel detector.

关键词： image image processing Features Point Edge Detector

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LFD-RSNet: Lightweight Face Detection Network for Rainy Weather Scenes

LFD-RSNet: Lightweight Face Detection Network for Rainy Weat...

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Natural Language processing (ICNLP), International conference on

作者： xiaoying Pan Yamie Ding Nijuan Zhang Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In response to the problem of low detection accuracy of lightweight models for face detection in complex rainy scenes, this paper proposes a face detection network FDL-RSNet for complex rainy scenes. This network integrates an adaptive image denoising module, an optimizer module, an HCA attention module, and a balanced branching strategy. The FDL-RSNet network can effectively address various challenges in complex rainy backgrounds, such as raindrop noise, differences in image lighting and contrast, and face detection challenges caused by low-quality images with dense targets. Experimental results on the UFDD Rain datasets show that FDL-RSNet has superior face detection capabilities in real-world unconstrained complex rainy scenes, Proved its effectiveness and reliability under such highly challenging conditions.

关键词： Adaptation models Rain Adaptive systems Noise Lighting Natural language processing Face detection Reliability Meteorology image denoising

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Curvature Continuous Path Planning with Reverse Searching for Efficient and Precise Autonomous Parking 25

Curvature Continuous Path Planning with Reverse Searching fo...

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IEEE 25th International conference on Intelligent Transportation systems (ITSC)

作者： Pang, Jinlong Zhang, Songyi Fu, Jiawei Liu, Jianyi Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

In autonomous driving, vehicles usually need to complete parking tasks. The existing parking planning algorithms based on hybrid A* can ensure the smoothness of the planned path, but it is inefficient due to the consumption of time. In addition, these algorithms do not fully consider the particularity of the parking scene, that is, the unbalanced distribution of obstacles around the lane and parking slot. To solve the above problems, we propose a curvature continuous parking planning method with reverse searching based on hybrid A*, called RS-HA*, which detects the collision of dense obstacles around the parking slot to prevent useless expansion nodes and complete the pruning of the algorithm to improve the real-time performance of the algorithm. We call this processing OCP (Obstacle-Collision-Pruning). RS-HA* can improve the planning efficiency and ensure the precision of the goal pose without reducing the smoothness of the path. We verified RS- HA* performance through simulation environment and real scene. The experimental results show that compared to the existing methods, RS-HA* greatly improves the parking efficiency and accuracy.

关键词： Search problems Real-time systems Path planning Planning Automobiles Task analysis Intelligent transportation systems

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Simulation Research and Development of Agricultural Products processing Process Based on Machine Learning Algorithm

Simulation Research and Development of Agricultural Products...

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2022 International conference on Artificial Intelligence and Autonomous Robot systems, AIARS 2022

作者： Peng, Luo Shihezi University School of Food Science and Technology Xinjiang Shihezi832003 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

In recent years, with the development of science and technology and its application in agricultural production, China's agricultural science and technology have made great progress, the concept of "agricultural processing"has been mentioned, and the research on agricultural processing has also achieved fruitful results. The combination of intelligent and automated machine learning algorithms with traditional industries can promote productivity improvement on the one hand, and realize industrial upgrading and transformation on the other hand. However, in practical production applications, machine learning algorithms are restricted by factors such as high cost, and the research and application of machine learning algorithms are greatly limited. With the development of virtual simulation technology in the field of machine learning algorithm research, it provides a new way for machine learning algorithm technology to be applied to agricultural product processing. Therefore, the research on machine learning algorithms has become a trend. The development of machine learning algorithms will drive the development of modern agriculture. It is very necessary for the research of machine learning algorithms to learn algorithms. The image is converted into a data matrix, and a computer used to replace the human brain is used to analyze the image, while completing a vision related task. China's agricultural development is facing severe challenges such as rising costs, continuous deterioration of the ecological environment and high tension of resource conditions. With the deepening of machine learning algorithm research and the rapid development of machine learning algorithm technology, machine learning algorithm simulation technology, as a safe and economic experimental tool in the application of machine learning algorithm technology, plays a more and more important role. In order to make full use of the latest research results abroad and narrow the gap with the advanced level ab

关键词： Deterioration

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