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LoRaWAN-based Camera with (CIRA) Compression and image Recovery Algorithm 7

LoRaWAN-based Camera with (CIRA) Compression and Image Recov...

引用

7th ieee World Forum on Internet of Things, WF-IoT 2021

作者： Zhang, John Y.Q. Yeung, Ben L. Wong, Joe C.Y. Cheung, Ray C.C. Lam, Alan H.F. City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong Sengital Limited Research and Development Department Hong Kong Hong Kong

ISBN: (纸本)9781665444316

LoRaWAN (Long Range Wide Area Network) is designed for long-range but low power consumption, low data rate, and low sampling frequency IoT applications. Many IoT applications (including agriculture, weather monitoring, rural area monitoring, smart city applications) have enormous demands on image or video capturing. A large amount of data, such as image, audio, and video, is not suitable to be transmitted through LoRaWAN. Although In this research, a compression and image recovery algorithm (CIRA) has been developed and implemented in a LoRaWAN-based camera system. The compression process of CIRA called CIRAJpeg can compress an image from a high-quality QVGA resolution sim 2.7 KB to sim 27 KB with an image recovery feature and transmit through the LoRaWAN network. With pixel position detection, CIRAJpeg can identify the position of lost packets. Then, CIRA uses the previously received image to recover the lost parts for the preview before. Those lost packets will be resent in the next sampling cycle to reconstruct the full image. CIRA provides user-friendly, long-range but low power consumption features for low sampling, low data loss application for high-quality QVGA image capturing through LoRaWAN. Experiments for different image transmissions by LoRaWAN are presented. © 2021 ieee.

关键词： Wide area networks

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DiffLight: Integrating Content and Detail for Low-light image Enhancement

DiffLight: Integrating Content and Detail for Low-light Imag...

引用

ieee Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yixu Feng Shuo Hou Haotian Lin Yu Zhu Peng Wu Wei Dong Jinqiu Sun Qingsen Yan Yanning Zhang Northwestern Polytechnical University Xi’an University of Architecture and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Low Light image Enhancement (LLIE) task has been a hotspot in low-level computer vision research. The camera sensor can only capture a small amount of ambient light signal in low-light condition, resulting in significant noise black pseudo artifacts in images, which not only degrade visual quality but also affect the performance of down-stream visual tasks. However, current methods often produce overly smoothed and distorted results, or introduce strong noise artifacts. Moreover, for recent UHD high-definition low-light images, due to GPU memory limitations, LLIE must be conducted in patches, leading to block artifacts. Faced with these challenges, we propose a dual-branch pipeline called DiffLight. Specifically, it consists of the Denoising Enhancement (DE) branch and the Detail Preservation (DP) branch. The DE-branch adopts a combination of DiffIR and LEDNet to reduce noise and enhance brightness, while the DP-branch utilizes a novel Light Full-Former (LFF) method, which comprises 20 Full-Attention (LFA) modules to preserve full-scale image details. To tackle block artifacts, we further introduce Progressive Patch Fusion (PPF) for patch fusion. Experimental results demonstrate that our approach is high-ranked in the CVPR2024 NTIRE Low Light Enhancement challenge and produced state-of-the (SOTA) results on other datasets.

关键词： visualization Computer vision image color analysis conferences Pipelines Noise reduction Graphics processing units

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Region-aware Half Wavelet Attention Network for Mixed Exposure Correction

Region-aware Half Wavelet Attention Network for Mixed Exposu...

引用

International conference on Signal processing Proceedings (ICSP)

作者： Jingwen Xia Chunjie Zhang Gaopeng Zhang Chuankai Liu School of Computer Science and Technology Beijing Jiaotong University Beijing China Key Laboratory of Spacecraft Optical Imaging and Measurement Technology of Xi’an Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi’an China Beijing Aerospace Control Center Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

images captured under different exposure conditions may contain both over- and under-exposures. Existing methods mainly focus on addressing over- or under-exposure problem in images. In this paper, we introduce an effective Region-aware Half Wavelet Attention Exposure Correction Network (RHECNet). Specifically, we introduce a region-based feature extraction module that effectively converts the region features in the mixed exposure scene into feature space, which is invariant to exposure. Besides, as incorrect exposure inevitably reduces structure information, we introduce an attention restoration block based on wavelet domain features. It combines exposure-invariant features with those extracted from the wavelet domain to restore structural information. RHECNet has competitive performance results on two image exposure Correction datasets in terms of quantitative metrics and visual quality.

关键词： Measurement visualization Wavelet domain Video sequences Signal processing Feature extraction image restoration Data mining

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Research on Key Technologies for Enhancing image Analysis Capability of Power Transmission and Transformation Equipment Based on Large visual Models

Research on Key Technologies for Enhancing Image Analysis Ca...

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Edge Computing and Scalable Cloud (EdgeCom), ieee International conference on

作者： Wangjun Zhang Yu Chen Xiong Wu Fan Li Hongshan Yang State Grid Shanghai Electric Power Company Shanghai China Transwarp Technology ShanghaiCo. Ltd Shanghai China

ISBN: (数字)9798350377132

ISBN: (纸本)9798350377149

This study significantly enhances the accuracy and efficiency of image analysis for power transmission and transformation equipment by introducing large model technology, bringing innovation to the field of intelligent inspection for power systems. Through an in-depth exploration of key aspects such as data preparation, model selection and training, and image analysis processing, this research employed a pre-trained large model based on the Transformer architecture. It was meticulously fine-tuned and trained according to the characteristics of power equipment images. By preprocessing and augmenting a vast collection of power transmission and distribution equipment images under various conditions, the model successfully extracted features, achieving high-precision defect identification and classification. The model was evaluated using cross-validation and an independent test set method, confirming its superiority over traditional image processing and existing intelligent inspection systems through metrics such as accuracy, recall, and F1 score. Experimental results have shown that this research method significantly outperforms traditional techniques and existing technologies on key performance indicators. Despite significant accomplishments, there remains room for enhancing the model’s ability to generalize and adapt to intricate environments. Upcoming efforts will delve into streamlined model training techniques and refine the model’s architecture to handle more intricate power image scenarios. Furthermore, the fusion of large model technology with other cutting-edge technologies will be explored to enable more astute, adaptable, and real-time scrutiny of power equipment. This study showcases the vast potential and practical worth of large model technology in power image analysis and intelligent inspection, poised to propel the intelligent evolution of the power sector.

关键词： Training Analytical models Adaptation models image analysis Computational modeling Power transmission Computer architecture

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Denoising Diffusion Adversarial Models for Unconditional Medical image Generation

Denoising Diffusion Adversarial Models for Unconditional Med...

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ieee Signal processing and communications Applications (SIU)

作者： Onat Dalmaz Baturay Saglam Gökberk Elmas Muhammad Mirza Tolga Çukur Elektrik ve Elektronik Muhendisliği Bölümü Bilkent Üniversitesi Ankara Türkiye Ulusal Manyetik Rezonans Araştirma Merkezi (UMRAM) Bilkent Üniversitesi Ankara Türkiye Sinirbilim Programi Mühendislik ve Fen Bilimleri Enstitüsü Bilkent Üniversitesi Ankara Türkiye

Unconditional medical image synthesis is the task of generating realistic and diverse medical images from random noise without any prior information or constraints. Synthesizing realistic medical images can enrich the quality and diversity of medical imaging datasets, which in turn, enhance the performance and generalization of deep learning models for medical imaging. Prevalent approach for synthesizing medical images involves generative adversarial networks (GAN) or denoising diffusion probabilistic models (DDPM). However, GAN models that implicitly learn the image distribution are prone to limited sample fidelity and diversity. On the other hand, diffusion models suffer from slow sampling speed due to small diffusion steps. In this paper, we propose a novel diffusion-based method for unconditional medical image synthesis, Diff-Med-Synth, that generates realistic and diverse medical images from random noise. Diff-Med-Synth combines the advantages of denoising diffusion probabilistic models and GANs to achieve fast and efficient image sampling. We evaluate our method on two multi-contrast MRI datasets and show that it outperforms state-of-the-art methods in terms of quality, diversity, and fidelity of the synthesized images.

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Research on blood cell image segmentation technology based on neural network method

Research on blood cell image segmentation technology based o...

引用

2021 International conference on Networking, communications and Information Technology, NetCIT 2021

作者： Deng, Tian Hubei College of Chinese Traditional Medicine Jingzhou434020 China

ISBN: (纸本)9781665400701

Computer image processing can be used for the processing of microscopic and biomedical images. For example, measuring and diagnosing microscopic images of blood cell smear by computer can improve work efficiency and reduce human error. In order to reduce artificial subjective interference, improve work efficiency and reduce workload, automatic analysis of cell images has become a hot spot in scientific research and clinical application. Among them, automatic segmentation of blood cell images has become one of the most interesting issues. A nonlinear system model based on improved BPNN(BP neural network) is proposed. The experimental results show that it can effectively overcome the unavoidable edge detection problem of existing methods, and make the segmented image closer to the real image. © 2021 ieee.

关键词： image segmentation

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Privacy-Preserving Detection Method for Transmission Line Based on Edge Collaboration

Privacy-Preserving Detection Method for Transmission Line Ba...

引用

ieee International conference on Trust, Security and Privacy in Computing and communications (TrustCom)

作者： Quan Shi Kaiyuan Deng School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Unmanned aerial vehicles (UAVs) are commonly used for edge collaborative computing in current transmission line object detection, where computationally intensive tasks generated by user nodes are offloaded to more powerful edge servers for processing. However, performing edge collaborative processing on transmission line image data may result in serious privacy breaches. To address this issue, we propose a secure single-stage detection model called SecYOLOv7 that preserves the privacy of object detecting. Based on secure multi-party computation (MPC), a series of secure computing protocols are designed for the collaborative execution of Secure Feature Contraction, Secure Bounding-Box Prediction and Secure Object Classification by two non-edge servers. Performance evaluation shows that both computational and communication overhead in this framework as well as calculation error significantly outperform existing works.

关键词： Power transmission lines Protocols image edge detection Collaboration Object detection Autonomous aerial vehicles Privacy breach

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Infrared-Augmented CycleGAN (IRACycle GAN) for Unpaired Night-to-Day image Translation 5

Infrared-Augmented CycleGAN (IRACycle GAN) for Unpaired Nigh...

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5th International conference on Advanced Research in Computing, ICARC 2025

作者： Fernando, K.J.P. Lakmal, H.K.I.S. Nirmal, W.C. Nsbm Green University Faculty of Engineering Department of Electrical Electronics & Systems Engineering Homagama Sri Lanka Nsbm Green University Faculty of Engineering Department of Mechatronic & Industrial Engineering Homagama Sri Lanka

ISBN: (纸本)9798331530983

This paper proposed Infrared-Augmented CycleGAN (IRACycle GAN), a novel extension of the traditional CycleGAN designed for unpaired Night-to-Day image translation. IRACycle GAN is a modified CycleGAN incorporating a 4-channel input architecture, simultaneously processing RGB and Infrared (IR) images. This dual-modality approach enables the model to capture visual features from the RGB images and critical thermal information, such as heat signatures and temperature variations in low light environments, by the IR images, significantly enhancing the realism and quality of generated Day RGB images. Unlike traditional CycleGAN, which relies solely on RGB data, IRACycle GAN's generator architecture processes RGB and IR inputs through separate pathways fused to create high-quality Day images. A key innovation is the modified cycle consistency loss, which ensures that thermal features from the IR input are preserved in the Day image, maintaining both visual fidelity and thermal context. Experimental results demonstrate IRACycle GAN's superior performance, with an NIQE score of 4.5, a BRISQUE score of 39.32, and an FID score of 15.5. These results highlight IRACycle GAN's ability to generate more natural, realistic, and contextually accurate images by effectively integrating RGB and IR data. The model's enhanced performance makes it suitable for surveillance, autonomous vehicles, and thermal mapping applications, where visual and thermal information are critical for accurate interpretation and decision-making in challenging environments. © 2025 ieee.

关键词： image enhancement

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Pre-processing Transformation for Enhancing the Transferability of Adversarial Examples 4

Pre-processing Transformation for Enhancing the Transferabil...

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4th International conference on communications, Information System and Computer Engineering, CISCE 2022

作者： Wang, Xiaotong Huang, Chunguang Gao, Fei Cheng, Hai Heilongjiang University College of Electronic and Engineering Harbin China ZTE Corporation Shanghai China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

Deep neural networks are susceptible to adversarial examples, which can cause misclassification of the models by adding some imperceptible perturbations to the original input images. Although most existing adversarial attack methods have achieved convincing success rates under white-box settings, they tend to exhibit weak transferability with the challenging black-box settings. Therefore, we propose a method based on input image pre-processing transformation named Shear & Pad Method (SPM) by optimizing the input diversity of the original images to generate adversarial examples. Meanwhile, it can alleviate the overfitting to improve the transferability of the adversarial examples. In addition, this method can be combined with related methods such as the family of fast gradient sign method to build stronger attack methods against the defense trained models. It can also be integrated into other transformation-based methods to generate more adversaries with better black-box transferability. Extensive experiments on the imageNet dataset show that our proposed method has higher success rates than existing baseline attack methods both on a single model and an ensemble of models. Therefore, we hope that our method can be used as an effective benchmark for evaluating the robustness of deep network models. © 2022 ieee.

关键词： Deep neural networks

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Performance Comparison of Vision Transformer-Based Models in Medical image Classification

Performance Comparison of Vision Transformer-Based Models in...

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ieee Signal processing and communications Applications (SIU)

作者： Elif Kanca Selen Ayas Elif Baykal Kablan Murat Ekinci Yazılım Mühendisliği Karadeniz Teknik Üniversitesi Trabzon Türkiye Bilgisayar Mühendisliği Karadeniz Teknik Üniversitesi Trabzon Türkiye

In recent years, convolutional neural networks have shown significant success and are frequently used in medical image analysis applications. However, the convolution process in convolutional neural networks limits learning of long-term pixel dependencies in the local receptive field. Inspired by the success of transformer architectures in encoding long-term dependencies and learning more efficient feature representation in natural language processing, publicly available color fundus retina, skin lesion, chest X-ray, and breast histology images are classified using Vision Transformer (ViT), Data-Efficient Transformer (DeiT), Swin Transformer, and Pyramid Vision Transformer v2 (PVTv2) models and their classification performances are compared in this study. The results show that the highest accuracy values are obtained with the DeiT model at 96.5% in the chest X-ray dataset, the PVTv2 model at 91.6% in the breast histology dataset, the PVTv2 model at 91.3% in the retina fundus dataset, and the Swin model at 91.0% in the skin lesion dataset.

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