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Dehazing images Using Vision Transformer Algorithm

Dehazing Images Using Vision Transformer Algorithm

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthik, Anyapu Reddy, B.Siva Chanikya Ranjani, M. Sandhia, G.K. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331509828

image dehazing is an important task to obtain clear images from blurry vision in low vision individuals. Although traditional methods and deep learning have made progress in this field, there are still challenges, especially in solving the real-life haze problem. This paper studies the application of Vision Transformer architecture, especially the Dehaze Former model, to improve the image dehazing performance. We present a new dataset that provides a comprehensive evaluation of the capabilities of Dehaze Former for difficult haze conditions (and perform function adjustments), and it outperforms existing models using state- of-the-art techniques when measured with new data. We propose several metrics to validate our findings, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Also a good comparison shows the effectiveness of this model in preserving image details and reducing artifacts. This research contributes to the advancement of image dehazing technology by demonstrating the ability of Vision Transformers to process grassy lawns. The insights gained from this study not only highlight the strengths of the Dehaze Former model, but also pave the way for future explorations into dehazing performance using Transformer-based architectures © 2025 IEEE.

关键词： Dataset Deep Learning image Dehazing Non-Homogeneous Haze Single image Restoration Vision Transformer

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Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval 29th

Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Te...

引用

29th international conference on Database systems for Advanced Applications (DASFAA)

作者： Liu, Haoyu Song, Yaoxian Wang, Xuwu Zhu, Xiangru Li, Zhixu Song, Wei Lie, Tiefeng Zhejiang Lab Res Ctr Intelligent Robot Hangzhou Peoples R China Zhejiang Univ Ctr X Mech Dept Engn Mech Hangzhou Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai Peoples R China

ISBN: (纸本)9789819755547;9789819755554

With the explosive growth of multi-modal information on the internet, unimodal search cannot satisfy the requirement of internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel LLM-based Query-enhanced method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://***/view/Flickr30K-cfq.

关键词： Text-image Retrieval Natural Query Compact and Fragmented Challenge Set Prompt-enhanced Method

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Research on high accuracy classification of temperature signals based on OFDR technology and EfficientNet 4

Research on high accuracy classification of temperature sign...

引用

4th international conference on Advanced Manufacturing technology and Electronic Information, AMTEI 2024

作者： Shang, Chuanyin Li, Peng Tong, Zhengrong Wang, Hao Li, Sibo Tianjin Key Laboratory of Thin Film Electronics and Communication Devices Engineering Research Center for Optoelectronic Devices and Communication Technology the Ministry of Education School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

The development of machine learning is driving revolutionary changes and unprecedented opportunities across various industries. This paper introduces a new temperature monitoring method that combines the efficient deep learning model EfficientNet with Optical Frequency Domain Reflectometry (OFDR). We utilize distributed fiber-optic sensing technology based on OFDR to collect one-dimensional temperature signals under different temperature conditions, which we then transform into a two-dimensional image format suitable for machine learning processing. The dataset is subsequently trained using the EfficientNet deep learning model. The results show that the trained model achieves a high accuracy rate of 97.5% on the test dataset. This research not only demonstrates the feasibility of efficiently and accurately monitoring temperature changes but also provides new solutions and technological pathways for areas such as environmental monitoring, industrial automation, and safety systems. © 2025 SPIE.

关键词： Education and training Data modeling Performance modeling Fiber optics sensors Machine learning Matrices Fiber optics image processing Reliability signal processing

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5th international Symposium on signal and image Processing, ISSIP 2024

5th International Symposium on Signal and Image Processing, ...

引用

5th international Symposium on signal and image Processing, ISSIP 2024

ISBN: (纸本)9789819795147

The proceedings contain 31 papers. The special focus in this conference is on signal and image Processing. The topics include: A Blockchain-based Secure Framework for Storage and Access of Surveillance Video Data;encountering the Challenges and Awareness of internet Security;Data-Driven Strategies for Twitter Engagement: Hashtag Recommendations and API Insights;Analyzing and Visualizing AI Decision-Making for Human-Centered Interaction and Trust;AI-ML-based Handover Techniques in Next-Generation Wireless Networks;castle Defender: Design and Implementation of Game based on Pygame;optimizing Healthcare: Enhancing Disease Management with Recommendation systems;optimizing Diabetes Prediction Models for Enhanced Health Data Processing;comparative Analysis of Classification Models Using Various Feature Sets;An Early Detection of Tomato Plant Disease with Deep Reinforcement Learning and CNN;ioT-Powered Health Monitoring System for Protecting Vital Organs Through Cloud-based Diagnosis;LASKV: Lightweight Authenticated Session Key Agreement for Video Surveillance System;Data Analyses of Factors Influencing Sustainable Adoption of Online Public Services: An Extended UTAUT Analysis in North Macedonia;a Comprehensive Review on Real-Time Campus Compass;deep Learning Approaches for Early Detection of Bovine Respiratory Diseases in Cattle;identifying Suitable Cloud Storage Services from Cross Cloud Platforms based on the Requirements;storage-Savvy Frame Recorder: Enhancing Storage Efficiency and Inspection Speed*;lossless Medical image Compression Using Block-Wise Burrows–Wheeler Transform and Modified Run Length Encoding;advancing Software Defect Detection and Prevention: Bridging Gaps in Early-Stage and Evolving Software systems;a New Way to Communicate: Deep Learning for Deaf and Dumb People;dependent Binomial: A Family of Distributions Derivable by Modifying a Base.

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River-GEM: Generating and Enhancing Muddy Water images

River-GEM: Generating and Enhancing Muddy Water Images

引用

2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Pramanick, Alik Satwik, Chivukula Sairam Kumar, Sonal Daydar, Akshay Sur, Arijit Indian Institute of Technology Guwahati India

ISBN: (纸本)9798350368741

Underwater image enhancement is crucial for marine engineering and aquatic robotics. However, most recent methods have focused on ocean environments, where they trained and tested on oceanic images. As a result, these methods are less effective in river water, where relatively blurry images are produced due to extensive muddy environments. In river water-based image restoration, we have observed two main limitations: (1) the lack of datasets that accurately represent the muddy water conditions found in river environments and (2) the limited effectiveness of current enhancement models in dealing with the unique challenges of muddy water images. To address these limitations, we introduce the "River-GEM" framework, which includes (1) a dataset of highly degraded muddy water images generated using a principle component analysis-based fusion technique and (2) a novel enhancement model utilizing the depth-map guidance to learn complex contextual details of muddy water images. The proposed model captures the intricate details of the objects in degraded images by analyzing feature correlation at multiple scales. Our model shows improvements of 3.42% in SSIM and 5.04% in PSNR over existing methods on the muddy-UIEB dataset. This dataset and enhancement network marks a significant step forward in this area of research. The dataset and code will be available at: https://***/Alik033/River-GEM. © 2025 IEEE.

关键词： image enhancement Muddy image generation principle component analysis transformer

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CGNet: Classification-Guided Multi-Task Interactive Network for Hyperspectral and Multispectral image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

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2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Lv, Guohua Xu, Yanlong Gao, Yongbiao Zhao, Guixin Wang, Guotao Sun, Xiangcheng Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets. © 2025 IEEE.

关键词： Feature interaction Hyperspectral classification Hyperspectral image image fusion

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FKAN-GMFNet: Fourier Kolmogorov-Arnold-based Group Multi-scale Fusion Network for Aneurysm image Segmentation

FKAN-GMFNet: Fourier Kolmogorov-Arnold-based Group Multi-sca...

引用

2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Pang, Shanchen Zhao, Xue Zhang, Yulin Zhao, Yawu Ding, Hengtao Zhao, Zhiyuan Qiao, Sibo Qingdao266580 China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao266590 China The College of Software Tiangong University Tianjin300387 China

ISBN: (纸本)9798350368741

KAN-based networks, while offering improved interpretability compared to traditional models used in medical image segmentation, often struggle with limited adaptability to diverse imaging environments, making them less ideal for such tasks. To address this issue, we propose a Fourier Kolmogorov-Arnold-based (FKAN) Group Multi-scale Fusion Network, termed FKAN-GMFNet, which incorporates an FKAN layer into a labeled intermediate representation, introducing an Enhanced-FKAN block. Furthermore, we develop the Attention Group Multi-scale Aggregation (ATGMA) module, which leverages attention mechanisms and grouping strategies to effectively fuse feature masks with both high- and low-scale feature information, thereby achieving a comprehensive multi-scale feature representation. Extensive experiments demonstrate that the FKAN-GMFNet significantly outperforms seven state-of-the-art methods in both Dice and IoU scores, where the Dice and IoU scores for the IAS-L dataset are 88.82% and 80.09%, respectively. Code is available at https://***/zx123868/FKAN-GMFNet. © 2025 IEEE.

关键词： Fourier Kolmogorov-Arnold Intracranial aneurysms Medical image Segmentation

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Multi-Domain image Translation Adversarial Network based on Artificial Intelligence 3

Multi-Domain Image Translation Adversarial Network Based on ...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Zhang, Lili College of Digital Art and Media Shandong University of Art & Design Jinan China

ISBN: (纸本)9798331508456

Traditional image translation methods often face difficulties such as differences in cross domain data distribution and difficulty in maintaining semantic consistency. Therefore, this article cites a generative adversarial network (GAN) based approach to capture mapping relationships between different domains. Firstly, this article adopts the Conditional Generative Adversarial Network (cGAN) framework to generate multi domain images. Next, this article introduces a shared encoder and multiple decoder structure. Finally, this article improved the performance of image translation results through optimization. In the experimental results, our method performed well on multiple standard datasets. The SSIM (Structural Similarity Index) value for the ink painting to oil painting task remains stable at around 0.88, and the PSNR (Peak signal to Noise Ratio) reaches 32.5 dB. In terms of diversity generation, this method also significantly improves the diversity and visual naturalness of image translation. Finally, the method proposed in this article significantly improves the quality and diversity of style transfer. © 2025 IEEE.

关键词： Generative adversarial networks

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Power internet of Things Access System based on Low Power Communication technology 14

Power Internet of Things Access System Based on Low Power Co...

引用

14th IEEE international conference on Communication systems and Network Technologies, CSNT 2025

作者： Jiang, Zhigang Ren, Liming Zhu, Bing Jiang, Jiafeng School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China School of Department of Electronic Information and Electrical Engineering Hefei University Anhui Hefei230000 China School of Computer and Information Hefei University of Technology Anhui Hefei230000 China School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014017 China

ISBN: (纸本)9798331531935

In response to the issues of data transmission energy efficiency and network coverage in the power internet of Things system, this study introduces an access scheme based on low-power communication technology. By optimizing the network architecture and communication protocol, the aim is to improve the energy efficiency of sensor nodes in the power internet of Things and reduce communication latency. Firstly, an access scheme based on low power wide area network (LPWAN) technology is designed. LoRa (Long Range Radio) or NB-IoT (Narrow Band internet of Thing) is selected as the communication protocol. By optimizing the working mode of sensor nodes, periodic transmission and event-driven mechanism are adopted. Secondly, an adaptive data transmission mechanism is designed to dynamically adjust the transmission frequency and rate according to environmental changes to ensure reliability under different network conditions. Furthermore, deep sleep mode and power consumption scheduling algorithm are adopted, and the network architecture adopts star topology to ensure the efficiency of signal coverage and data forwarding, and the secure transmission of data information is guaranteed by encryption and authentication mechanism. Finally, through simulation and actual deployment, the performance of the scheme in the actual environment is verified, and its stability and data transmission efficiency under different network scales are evaluated. The battery life of periodic wake-up mode is significantly lower than that of deep sleep mode. Under low-frequency transmission (once an hour), the battery life is 320 days. In periodic wake-up mode, the average power consumption of sensor nodes is significantly higher than that of deep sleep mode. At low frequency transmission, average power consumption is 3.5 mW and battery life drops to 75 days. The power internet of Things access system based on low-power communication technology proposed in this work effectively solves the bottleneck pr

关键词： internet of things

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ETIS international conference on Emerging Technologies for Intelligent systems, ETIS 2025

ETIS International Conference on Emerging Technologies for I...

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2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

ISBN: (纸本)9798331507541

The proceedings contain 104 papers. The topics discussed include: intelligent traffic signal management system;an automated pothole detection and repair system for effective road maintenance and safety;on-board data acquisition system for spacecraft;cyber-physical modeling and simulation of four-axis articulated robot;an intelligent system for prediction of depression based on facial emotions and textual data;evaluating the impact of dataset variability on Malayalam emotion detection system;an automated smart incubator system for poultry farms and hatcheries;image-driven face presentation attack detection in biometric systems;and soft computing approaches in enhancing healthcare data security using deep neural networks and reversible data hiding.

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