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A ConvNeXt-backbone cGAN for Underwater Power Engineering image Enhancement 13

A ConvNeXt-backbone cGAN for Underwater Power Engineering Im...

引用

13th international conference on Power and Energy systems, ICPES 2023

作者： Chen, Tiebin Zhu, Suxun Ren, Bowen Chen, Laijun Industry Research Center Xining China Sichuan Energy Internet Research Institute Chengdu China

ISBN: (纸本)9798350345063

Underwater images is often greatly affected by color distortion, decreased contrast, and blurred details during the construction and maintenance of underwater power engineering. we proposed an underwater power engineering image enhancement model based on conditional generative adversarial networks (cGAN). Firstly, the generator incorporates the ConvNeXt module to enhance the global feature extraction capability by utilizing large convolutional kernels specifically designed for underwater image enhancement tasks. Subsequently, a boundary loss function is formulated to better capture high frequency information related to local textures and styles by comparing the boundary differences in the images. Lastly, a style transfer method is employed to transform land power engineering images and combine them with underwater environmental images to create a novel dataset for underwater power engineering. Case study results demonstrate the effectiveness of the proposed model in improving the quality of underwater power engineering images through adversarial learning, surpassing the enhancement performance of conventional models. © 2023 ieee.

关键词： image enhancement

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Unmasking Emotions: Deep Neural Networks for image-based Emotion Recognition 2

Unmasking Emotions: Deep Neural Networks for Image-Based Emo...

引用

2nd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2024

作者： Sohel, Mohammad Aman Madhukar, Raghupatruni Sai Batchu, Sai Muralidhar Jyotsna, C. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350327533

Recognizing human emotions is a multifaced field that uses the technology, especially deep learning and Convolutional neural networks, to recognize and understand emotions such as happiness, sadness, anger, disgust and fear. Convolutional Neural Networks are designed for visual data explorations and they are effectively capture facial expressions for emotion recognition. The process involves the collection of labeled data, and also preprocesses it, creating an appropriate deep neural network, training the model with constant updates, and assessing its performance using metrics on a testing dataset. Proposed work tackles emotion detection in irregularly distributed datasets of preprocessed 48x48 grayscale emotion images. Significant performance gains are achieved through targeted data augmentation for marginalized classes, giving valuable insights for robust emotion recognition models. Optimizing hyperparameters enhances the model, enabling its deployment in real-world scenarios by ensuring integration, data handling, and real-Time functionality. The proposed model primarily focuses on offline image and video capturing for emotion detection, and highlighting on video-emotion recognition. © 2024 ieee.

关键词： Emotion Recognition

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A Super-Resolution Attention Feedback Network

A Super-Resolution Attention Feedback Network

引用

2023 ieee international conference on Sensors, Electronics and Computer Engineering, ICSECE 2023

作者： Wang, Jiahui Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9798350313734

Since the internet and social media grow rapidly in recent years. image processing is playing an increasingly important role in daily life. A key task for image processing is super-resolution. In a super-resolution task, an image of a high-resolution rate would be built on the basis of a low-resolution picture. Super-resolution technologies could help recover damaged images and recognize the detail of the images. As neutral networks develop quickly, many industries have used deep learning to improve their efficiency. Since neutral networks have a good performance in image processing, they quickly become the dominant methods in super-resolution tasks. Unlike traditional methods, deep learning could work better and more effectively. based on the super-resolution feedback network (SFBEN), a channel attention mechanism has been added to the feedback block of the network. It is all known that attention mechanisms and feedback are two important ways for a human to deal with an image. This paper combines these mechanisms in the network. The new channel attentional layer uses a convolutional layer to generate a weight matrix and then adjust the input based on the matrix. The application of this channel attentional block reduces the training loss of the whole network and increases the peak signal-to-noise ratio. © 2023 ieee.

关键词： image processing

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FPGA-based Multi-Channel image Acquisition and Processing System 2

FPGA-Based Multi-Channel Image Acquisition and Processing Sy...

引用

2nd international conference on signal Processing and Intelligent Computing, SPIC 2024

作者： Chu, Xiaohui Jiang, Qiang Zhang, Zhichao Chen, Tong Wang, Xin Yuan, Yi Shenyang Ligong University School of Automation and Electrical Engineering Shenyang110159 China Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9798350368888

The deep integration of new-generation information technology and manufacturing is triggering far-reaching industrial changes. Machine vision inspection is widely used in large-scale repetitive industrial production processes. In order to solve the problem of the limited field of view of the machine vision inspection system and the obstruction of the camera view during the inspection process, this article takes the glue dispenser vision inspection system as the project background, and designs a high-performance and low-cost FPGA-based multi-channel image acquisition and transmission system scheme. The system can realize parallel acquisition, decoding, storage, display, and transmission of data from 4 image sensors. The OV5640 is used as the video sensor for video capture, and video data is cached using DDR3 SDRAM and transmitted to the host computer through Gigabit Ethernet communication technology. This thesis utilizes the advantages of FPGA in the field of image processing to perform real-time pipeline operations on video data, solving the limitation of video capture by a single camera and improving the system's real-time performance. Implementing multi-channel image grayscale conversion, filtering, edge detection, and stitching fusion in FPGA. © 2024 ieee.

关键词： Inspection

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RAMMM: A Rapid Attention-based Multimodal Modification Model for Massive image Generation 29

RAMMM: A Rapid Attention-Based Multimodal Modification Model...

引用

29th ieee international conference on Parallel and Distributed systems, ICPADS 2023

作者： Xu, Zhenyuan Chen, Xuewen Wang, Taotao Quan, Wei Zhu, Yi Nanjing Audit University Nanjing211815 China

ISBN: (纸本)9798350330717

Nowadays, deep learning (DL) techniques have found extensive applications in the internet of Things (IoT) community, such as autonomous driving and medical diagnosis. Despite these successful implementations, elevated data collection expenses, data confidentiality concerns, and privacy issues all contribute to the complexity and costliness of data acquisition in DL-based image processing. image modification methods based on diffusion models can subtly modify images to generate new images with similar content to the original image but with unique differences, providing a powerful tool for data augmentation. Nonetheless, diffusion models exhibit constraints in cross-modal image modification, such as sensitivity to prompt, model complexity, and user-friendliness. To address these issues, this paper proposes a rapid attention-based multimodal modification model (RAMMM) to facilitate straightforward and efficient text-guided image modification. Our proposed RAMMM primarily enhances the text processing and image generation procedures via an attention mechanism, thus improving the ability in capturing semantic and contextual information within the text. Consequently, RAMMM excels in generating high-quality sample images aligned with the description provided for the modified text. In addition, by utilizing text-guided image modification, RAMMM is capable of generating batches of image samples systematically to augment the dataset size. Evaluation results demonstrate that RAMMM can improve the performance and generalization capabilities of diffusion models by enhancing both the quality and quantity of the dataset. When employed for data augmentation on the CIFAR10 dataset, RAMMM achieves a 1.5% increase in target recognition accuracy. © 2023 ieee.

关键词： attention mechanism data augmentation deep learning image modification

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Language-Oriented Semantic Communication for image Transmission with Fine-Tuned Diffusion Model 16

Language-Oriented Semantic Communication for Image Transmiss...

引用

16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Wei, Xinfeng Tong, Haonan Yang, Nuocheng Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Network Beijing China

ISBN: (纸本)9798350390643

Ubiquitous image transmission in emerging applications brings huge overheads to limited wireless resources. Since that text has the characteristic of conveying a large amount of information with very little data, the transmission of the descriptive text of an image can reduce the amount of transmitted data. In this context, this paper develops a novel semantic communication framework based on a text-2-image generative model (Gen-SC). In particular, a transmitter converts the input image to textual modality data. Then the text is transmitted through a noisy channel to the receiver. The receiver then uses the received text to generate images. Additionally, to improve the robustness of text transmission over noisy channels, we designed a transformer-based text transmission codec model. Moreover, we obtained a personalized knowledge base by fine-tuning the diffusion model to meet the requirements of task-oriented transmission scenarios. Simulation results show that the proposed framework can achieve high perceptual quality with reducing the transmitted data volume by up to 99% and is robust to wireless channel noise in terms of portrait image transmission. © 2024 ieee.

关键词： Semantics

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Deep Video Demoireing via Compact Invertible Dyadic Decomposition

Deep Video Demoireing via Compact Invertible Dyadic Decompos...

引用

ieee/CVF international conference on Computer Vision (ICCV)

作者： Quan, Yuhui Huang, Haoran He, Shengfeng Xu, Ruotao South China University of Technology School of Computer Science and Engineering Guangzhou 510006 China Pazhou Lab Guangzhou 510335 China Singapore Management University School of Computing and Information Systems 188065 Singapore

ISBN: (纸本)9798350307184

Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling the features of image patterns from that of moire patterns at different scales, while retaining all latent image features to facilitate reconstruction. In addition, a compressed form for the networks output is introduced to reduce computational complexity and alleviate overfitting. The experiments show that CIDNet outperforms existing methods and enjoys the advantages in model size and computational efficiency.

关键词： Deep Video Computational Efficiency image Features Model Size Video Sequences image Pattern Latent Features Compressed Format Complex Texture Multiscale Process Latent image Deep Neural Network Attention Mechanism Video Frames Peak signal-to-noise Ratio Multiscale Analysis Feature Alignment Adjacent Frames Supplement For Details Discrete Cosine Transform Frame Alignment U-shaped Structure Alignment Blocks Multi-scale Structure Reversible Loss Color Shift Information Fidelity Latent Code Spatial Pyramid Pooling Spatial Pyramid

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Weight-based Regularization for Improving Robustness in image Classification

Weight-based Regularization for Improving Robustness in Imag...

引用

ieee international conference on Multimedia and Expo (ICME)

作者： Yang, Hao Wang, Min Yu, Zhengfei Zhou, Yun Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

ISBN: (纸本)9781665468916

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks. Recently, Stochastic Neural Networks (SNNs) have been proposed to enhance adversarial robustness by injecting uncertainty into the models. However, existing SNNs often inspired by intuition and rely on adversarial training, which is computationally costly. To address this issue, we propose a novel SNN called the Weight-based Stochastic Neural Network (WB-SNN), which is based on optimizing an error upper bound of adversarial robustness from the perspective of weight distribution. To the best of our knowledge, we are the first to propose a theoretically guaranteed weight-based stochastic neural network without relying on adversarial training. In comparison to normal adversarial training, our method saves about three times the computation cost. Extensive experiments on various datasets, networks, and adversarial attacks have demonstrated the effectiveness of the proposed method.

关键词： stochastic neural network adversarial robustness adversarial examples

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Moire Vision: A signal Processing technology Beyond Pixels Using the Moire Coordinate 15

Moire Vision: A Signal Processing Technology Beyond Pixels U...

引用

15th ieee international conference on Computational Photography (ICCP)

作者： Jo, Kensei Inami, Masahiko Univ Tokyo Tokyo 1138654 Japan

ISBN: (纸本)9798350316766

Nowadays, many computer vision (CV) techniques require high-precision feature point detection in calibration targets. Checkerboards or circle grids are common patterns used to infer the locations of the points directly from the image. However, such approaches can lead to degradation in sub-pixel accuracy for low-resolution or sub-sampled sensor data (e.g., Bayer pattern). This paper proposes a target-based calibration technique applicable to various conventional CV methods, leveraging the moire phenomena. We use a moire pattern that is created between the digital camera's pixels and the high-frequency texture on the calibration target. Even a tiny movement of the target or camera leads to a significant motion of the moire, acting as a magnifier of the target texture for estimating accurate location of targets or camera. In contrast to previous moire approaches that employ frequency domain analyses, we introduce the moire coordinates and derive them from the difference between the sensor and target texture position. In the moire coordinate, each moire pattern is undistorted. Unlike moire in standard images, each moire pattern has almost the same scale and shape in these coordinates. Utilizing this property, we propose a straightforward algorithm to detect and handle correspondence in the moire peaks in image and texture location, treating them as feature points of calibration target. In addition, the moire vision can magnify pixels, which means the pattern in the moire coordinate visualizes the pixel sensitivity with high spatial resolution. To validate the proposed approach, we present an empirical evaluation using simulated and real Bayer sensor patterns. We show the advantages of the proposed method in terms of accuracy and identify promising future directions.

关键词： Moire Computer Vision Computational Photography image Processing Calibration Marker

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AIOIML: Automatic Integration of Ontologies for IoT Domain Using Hybridized Machine Learning Techniques 2

AIOIML: Automatic Integration of Ontologies for IoT Domain U...

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2nd international conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and signal Processing, PCEMS 2023

作者： Shrivastava, Rishi Rakesh Deepak, Gerard Birla Institute of Technology and Science Department of CSE Pilani India Manipal Institute of Technology Bengaluru Department of CSE Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350310719

There is a need for Ontology modelling and automatic generation of Ontologies in order to assimilate knowledge World Wide Web knowledge as a strategic model. Ontologies are the best knowledge descriptor model as they have some amount of human cognition associated with them because either humans are major contributors when they are generated manually or are indirect contributors when they are semi automatically generated. internet of Things is a domain which has strategically evolved in the last few years, and there is a need for integrating several facets of internet of Things Ontology. In this paper a strategic scheme for internet of Things Ontology integration for internet of Things domain with different perspective are proposed wherein the dataset are subjected to tag generation which is further classified using the AdaBoost classifier which are aligned with the random core classes of the existing variational Ontologies in the internet of Things domain using Shannon's entropy and the pointwise mutual information measure with differential step deviation measure. Which yields average precision and recall of 96.83 and 97.95 respectively. © 2023 ieee.

关键词： internet of things

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