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检索条件"任意字段=Third International Conference on Image Processing and its Applications"

共 10035 条记录，以下是31-40 订阅

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REDEFINING NIGHT VISION: THE POWER OF MSR-DRIVEN NEURAL ISP 49

REDEFINING NIGHT VISION: THE POWER OF MSR-DRIVEN NEURAL ISP

引用

49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hou, Jingchao He, Guanghui Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

This paper addresses two key limitations in existing image Signal processing (ISP) approaches: the suboptimal performance in low-light conditions and the lack of trainability in traditional ISP methods. To tackle these issues, we propose a novel, trainable ISP framework that incorporates both the strengths of traditional ISP techniques and advanced MultiScale Retinex (MSR) algorithms for night-time enhancement. Our method consists of three primary components: an ISP-based Luminance Harmonization layer to initially optimize luminance levels in RAW data, a deep learning-based MSR layer for nuanced decomposition of image components, and a specialized enhancement layer for both precise, regionspecific luminance enhancement and color denoising. The proposed approach is validated through rigorous experiments on machine vision benchmarks and objective visual quality indicators. Our results demonstrate not only a significant improvement over existing methods but also robust adaptability under diverse lighting conditions. This work offers a versatile ISP framework with promising applications beyond its immediate scope.

关键词： image Signal processing (ISP) Multi-Scale Retinex (MSR) Enhancement Reflection and Illumination Separation

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FRACTIONAL ORDER SPECTRUM IN SAR image REGISTRATION

FRACTIONAL ORDER SPECTRUM IN SAR IMAGE REGISTRATION

引用

IEEE international conference on Multimedia and Expo (ICME)

作者： Sun, Jianjun Zhao, Yan Li, Xinbo Wang, Shigang Wei, Jian Wang, Shibo Jilin Univ Coll Commun Engn Changchun Peoples R China

ISBN: (纸本)9798350390155;9798350390162

SAR image registration is an important processing procedure for change detection and target recognition. However, the registration performance is seriously influenced by Symmetric a Stable ( SaS) noises in SAR images. In order to cancel the impact of SaS noises in SAR image applications, a new concept of Fractional Order Spectrum of Cumulant (FOS-C) and SAR image registration based on (FOS-C) are proposed for the first time in this paper. In the proposed method, the images are registered from coarse to precise in three steps. First, the coarse registration based on Fourier Transform is used to estimate the scaling and rotation differences between images. Second, the registration based on FOS-C is designed, and used to provide the rough position of the similar regions. third, the normalization crosscorrelation (NCC) algorithm based on FOS-C is used to achieve the fine registration. Experimental results show that our method outperforms SAR- SIFT and KAZE- SAR.

关键词： SAR image registration Fractional Order Spectrum of Cumulant Symmetric a Stable noise noise suppression.

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Overview of Deep Learning image processing 2

Overview of Deep Learning Image Processing

引用

2nd IEEE international conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Zhai, Haoqi Huang, Zishen Lv, Xin Lv, Biyun Xi'an Polytechnic University Xi'an China Guangxi Arts University Nanning China

ISBN: (纸本)9798350373646

In the last decade, deep learning has rapidly advanced and achieved significant breakthroughs in the field of image processing. This survey aims to provide a comprehensive overview of recent advancements in deep learning-based image processing technology. Initially, it introduces the fundamental principles of deep learning and commonly used network architectures, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and the ELM-RVFL series. These networks have found widespread applications in image processing tasks. Subsequently, the survey examines the current state of deep learning in image classification, object detection, and image segmentation, where deep learning models have demonstrated exceptional performance, surpassing traditional methods in terms of accuracy and efficiency. Furthermore, it explores various applications of deep learning in areas such as face recognition, medical imaging, and pedestrian detection, leading to breakthroughs in accuracy and robustness. Finally, the survey explores the symbiotic relationship between deep learning and image processing, highlighting potential future directions for new deep models and training methods. These insights aim to inspire researchers to explore innovative approaches in deep learning for image processing, ultimately advancing image processing technology and its diverse applications. © 2024 IEEE.

关键词： Deep learning image classification image processing image segmentation Target detection

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Underwater image Enhancement for Depth Estimation via Various image processing Techniques

Underwater Image Enhancement for Depth Estimation via Variou...

引用

international conference on System Science and Engineering (ICSSE)

作者： Thanh Nguyen Canh Minh DoNgoc Truong Nguyen Quang Huong Bui Thanh Xiem HoangVan Vietnam Natl Univ Univ Engn & Technol Hanoi Vietnam Hanoi Univ Civil Engn Hanoi Vietnam

ISBN: (数字)9798350359886

ISBN: (纸本)9798350359893;9798350359886

Depth estimation is crucial for autonomous underwater vehicles (AUVs) to navigate effectively and understand their surroundings. While deep neural networks offer promising solutions for depth estimation, their efficacy in challenging underwater environments-characterized by low light conditions and noise-is limited. To address this challenge, this paper proposes a method to enhance underwater image quality, thereby bolstering the accuracy of depth estimation. The approach involves preprocessing underwater images using color compensation and light balancing techniques, alongside contrast and sharpness enhancements. Subsequently, depth estimation is performed utilizing the Udepth model on the enhanced images. The efficacy and accuracy of the proposed method are evaluated and presented, demonstrating its potential to enhance depth image quality for underwater robotics applications.

关键词： Underwater image enhancement Depth estimation Deep learning image processing

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HISTOHDR-NET: HISTOGRAM EQUALIZATION FOR SINGLE LDR TO HDR image TRANSLATION 31

HISTOHDR-NET: HISTOGRAM EQUALIZATION FOR SINGLE LDR TO HDR I...

引用

2024 international conference on image processing

作者： Barua, Hrishav Bakul Krishnasamy, Ganesh Wong, KokSheik Dhall, Abhinav Stefanov, Kalin Monash Univ Malaysia Sch Informat Technol Subang Jaya Malaysia TCS Res Robot & Autonomous Syst Lab Kolkata India Flinders Univ S Australia Coll Sci & Engn Adelaide SA Australia Monash Univ Fac Informat Technol Clayton Vic Australia

ISBN: (纸本)9798350349405;9798350349399

High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are overor under-exposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with self-attention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods.

关键词： Data fusion Self-attention High dynamic range imaging Deep learning Histogram equalization

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Key Insight Into Enhancing Medical image processing Through ChatGPT: Future Direction and applications

Key Insight Into Enhancing Medical Image Processing Through ...

引用

2024 international conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Verma, Kavery Srivastava, Subodh Mishra, Ritesh Kumar National Institute of Technology Department of Ece Bihar Patna800005 India

ISBN: (纸本)9798350379716

Medical image processing is undergoing a remarkable transformation with integration of Natural Language processing (NLP) and Artificial Intelligence (AI) in the healthcare industry. An advanced NLP model known as Chat Generative Pre-Trained Transformer (ChatGPT) is investigated in this article based on its potential applications in various aspects of medical imaging. Here, the fundamental architecture of the most recent version of ChatGPT, version 4, is discussed in relation to medical image processing. Additionally, the possible advantages, difficulties, and future prospects of using ChatGPT to assist in the analysis of image data, diagnosis, and optimization of workflow have been discussed here. © 2024 IEEE.

关键词： Artificial Intelligence (AI) ChatGPT GPT Healthcare and Medical image processing

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PHYSICALLY-CONSTRAINED BLOCK-TERM TENSOR DECOMPOSITION FOR POLARIMETRIC image RECOVERY 49

PHYSICALLY-CONSTRAINED BLOCK-TERM TENSOR DECOMPOSITION FOR P...

引用

49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Barreto, Saulo Cardoso Flamant, Julien Miron, Sebastian Brie, David Univ Lorraine Cran CNRS F-54000 Nancy France

ISBN: (纸本)9798350344868;9798350344851

This paper introduces a complete approach for the recovery of polarimetric images from experimental intensity measurements. In many applications, such images collect, at each pixel, a Stokes vector encoding the polarization state of light. By representing a Stokes vector image as a third-order tensor, we propose a new physically-constrained block-term tensor decomposition called Stokes-BTD. The proposed model is flexible and comes with broad identifiability guarantees. Moreover, physical constraints ensure meaningful interpretation of low-rank terms as Stokes vectors. In practice, Stokes images must be recovered from indirect, intensity measurements. To this aim, we implement two recovery algorithms for Stokes-BTD based on constrained alternated optimization and highlight constraints related to Stokes vectors. Numerical experiments on synthetic and real data illustrate the potential of the approach.

关键词： block-term tensor decomposition Stokes polarimetric imaging alternated constrained optimization

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Experimental Study on the Accuracy and Precision of Object Size Measurement with Dual-Lens Imaging Systems 48

Experimental Study on the Accuracy and Precision of Object S...

引用

48th Annual IEEE international Computers, Software, and applications conference (COMPSAC) - Digital Development for a Better Future

作者： Hsu, Tse-Chuan Cheng, Yu-Chia Soochow Univ Dept Comp Sci & Informat Management Taipei Taiwan

ISBN: (纸本)9798350376975;9798350376968

The demand for automated dimensional measurement technology is growing daily with global supply chain management challenges. Accurate dimensional measurement is critical to optimizing packaging design, reducing shipping costs, improving warehouse efficiency, and enhancing the inventory management process. Traditional manual measurement methods are time-consuming and error-prone due to measurement and data collection errors. There are hidden concerns about the accuracy of the data, and they cannot meet the fast-paced, high-efficiency demands of modern logistics. To address this problem, this study introduces an automatic three-dimensional object size measurement system based on dual-view imaging technology. The experimental environment uses top-angle and side-angle lenses, combined with the image processing technology of the OpenCV framework, to accurately measure the object's outermost three-dimensional dimensions. The image processing algorithm used in the research can automatically identify the outline of an object and calculate its length, width, and height. The development of the system includes precise camera calibration, simultaneous image acquisition, and image processing technology to ensure the accuracy and consistency of measurement results. The research demonstrates the experimental verification of accurately using images to calculate the contour feature information of objects. It conducts measurements based on various samples, proving that it can measure the outer dimensions of objects of various materials and shapes and has strong practicability and broad application prospects. The contribution of the research not only provides new solutions for dimensional measurement in the logistics field but also the experimental empirical model will expand the development of logistics warehouse automation systems with innovative and forward-looking data calculation and application.

关键词： Computer Vision Dual-Angle image Analysis OpenCV Three-Dimensional Measurement

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Clearing Text images: A Non-blind Deblurring with Convex Total Variation Regularization Model 7

Clearing Text Images: A Non-blind Deblurring with Convex Tot...

引用

7th IEEE international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Kumar, Narendra Bhatnagar, Gaurav Indian Inst Technol Jodhpur Dept Math Jodhpur India

ISBN: (纸本)9798350351439;9798350351422

In response to the escalating issue of obscured text in forensic analysis due to various factors such as illumination changes and low-resolution images, this paper introduces a novel convex penalty function within a total variation regularization model for text image deblurring. The proposed model enhances sparsity and edge preservation, yielding superior deblurring results. Accompanying this, an efficient Alternating direction method of multiplier algorithm is proposed, showcasing rapid convergence for complex deblurring tasks. Extensive experimentation validates the model's efficacy across diverse image resolutions, demonstrating superior performance in both qualitative and quantitative assessments, particularly in edge preservation and sparsity achievement. Additionally, the model exhibits superior numerical convergence compared to existing techniques, establishing its reliability for text image deblurring applications.

关键词： Alternating direction method of multipliers Convex Optimization image deblurring Total variation Sparsity

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applications and Challenges of Blockchain Technology in image processing: A Comparative Review 2nd

Applications and Challenges of Blockchain Technology in Imag...

引用

2nd international conference on Innovations in Data Analytics, ICIDA 2023

作者： Agnihotri, Abhishek Prabhakar, Abhishek Pandey, Kapil Kumar Department of Information Technology Dr. Ambedkar Institute of Technology for Divyangjan Kanpur India

ISBN: (纸本)9789819734658

With the increase in internet usage, blockchain technology has risen in popularity in recent years as one of the hottest technologies. It uses decentralized architecture with distributed database and provides cryptography thus giving integrity of data without using a third party. It is used to increase security and privacy in various applications. There is an expanding variety of applications not only in finance but also important to image processing, even though they were initially developed for and most frequently used with cryptocurrencies. This paper provides examination of blockchain technology in image processing. It also discusses potential implementation difficulties as well as the ideas that underlie the technology and applications pertinent to image processing. This paper intensively analyzes the existing research work, provides issues in blockchain for image processing, and provides refined observation of each problem so that future research can be conducted efficiently. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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