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检索条件"任意字段=Conference on Visual Communications and Image Processing"

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Infrared-to-Optical image Translation for Keypoint-Based image Registration 30

Infrared-to-Optical Image Translation for Keypoint-Based Ima...

引用

30th IEEE Signal processing and communications Applications conference (SIU)

作者： Elsaeidy, Mohamed Erkol, Muhammed Emin Gunturk, Bahadir Kiirsat Ates, Hasan Fehmi Istanbul Medipol Univ Dept Comp Engn Istanbul Turkey Istanbul Medipol Univ Dept Elect & Elect Engn Istanbul Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

Multi-modal image registration is a critical step in many remote sensing and visual navigation applications. While image registration techniques developed for single modality images do not perform well for multi-modal registration, techniques developed for multi-modal image registration are not suitable for real-time applications. In this study, we aim to achieve real-time registration of infrared and optical (visible range) images. Specifically, we investigate the use of image-to-image translation deep network to convert infrared images to optical images, which is then applied for keypoint-based image registration.

关键词： Multi-modal image registration GAN image translation network

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visual transmission and analysis of graphic language based on image processing technology 2

Visual transmission and analysis of graphic language based o...

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2nd Asia-Pacific conference on communications Technology and Computer Science, ACCTCS 2022

作者： Wei, Li Chunli, Wu Changchun Humanities and Sciences College Jilin Province Changchun City130117 China

ISBN: (纸本)9781665400343

The application of computer technology has also brought many conveniences to people. As an important part of computer technology, the application of image processing technology in visual transmission system has created rich visual effects for people, and also brought about technical changes for visual transmission design. In this paper, a visual transmission and analysis method of graphic language based on image processing technology is proposed. Enhance the low-light image in HSV color space, optimize the visual transmission design of graphic language by combining tone mapping and color fusion technology, and realize the color separation design of graphic language under the visual transmission model. The simulation results show that the color balance of color separation design of graphic language with this method is better, the visual expression ability of the designed graphic language is stronger, and the image quality level of the pattern is higher. © 2022 IEEE.

关键词： Color

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LLM-CXR: INSTRUCTION-FINETUNED LLM FOR CXR image UNDERSTANDING AND GENERATION 12

LLM-CXR: INSTRUCTION-FINETUNED LLM FOR CXR IMAGE UNDERSTANDI...

引用

12th International conference on Learning Representations, ICLR 2024

作者： Lee, Suhyeon Kim, Won Jun Chang, Jinho Ye, Jong Chul Korea Advanced Institute of Science & Technology Korea Republic of

Following the impressive development of LLMs, vision-language alignment in LLMs is actively being researched to enable multimodal reasoning and visual input/output. This direction of research is particularly relevant to medical imaging because accurate medical image analysis and generation consist of reasoning based on a combination of visual features and prior knowledge. Many recent works have focused on training adapter networks that serve as an information bridge between image processing (encoding or generating) networks and LLMs;but presumably, in order to achieve maximum reasoning potential of LLMs on visual information as well as language, image and text features should be allowed to interact more freely. This is especially important in the medical domain because understanding and generating medical images such as chest X-rays (CXR) require not only accurate visual and language-based reasoning but also a more intimate mapping between the two modalities. Thus, taking inspiration from previous work on the transformer and VQ-GAN combination for bidirectional image and text generation, we build upon this approach and develop a method for instruction-tuning an LLM pre-trained only on text to gain vision-language capabilities for medical images. Specifically, we leverage a pretrained LLM's existing question-answering and instruction-following abilities to teach it to understand visual inputs by instructing it to answer questions about image inputs and, symmetrically, output both text and image responses appropriate to a given query by tuning the LLM with diverse tasks that encompass image-based text-generation and text-based image-generation. We show that our model, LLM-CXR, trained in this approach shows better image-text alignment in both CXR understanding and generation tasks while being smaller in size compared to previously developed models that perform a narrower range of tasks. © 2024 12th International conference on Learning Representations, ICLR 2024. All r

关键词： Query processing

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Filling the Gap: Enhancing Ultra-Low Light image Brightness Through Multi-Band NIR Estimation

Filling the Gap: Enhancing Ultra-Low Light Image Brightness ...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Park, Jeong-Hyeok Park, Tae-Sung Kim, Jong-Ok Korea University School of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

We propose a method for capturing high-quality images in low-light environments using multi-band near-infrared (NIR) images, which offer robustness to brightness variations and provide structural information not present in RGB images. As multi-band NIR images are not commonly available in consumer cameras, an RGB-NIR conversion network is employed to estimate them. Training data comes from a new low-light dataset with NIR-RGB image pairs. Unlike single-band approaches, this method utilizes multi-band NIR images with peak wavelengths of 785nm, 850nm, and 940nm, resulting in enhanced quality. Integration of the RGB-NIR conversion network into an existing low-light enhancement (LLIE) network is achieved using the proposed cross-attention transformer. Experimental results validate the effectiveness of multi-band NIR estimation in enhancing low-light intensity. Moreover, the proposed RGB-NIR conversion network and cross-attention module can be applied to any existing deep LLIE networks. © 2023 IEEE.

关键词： image enhancement

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Feature Decomposition and Attribute Augmentation for Attribute-based Person Search

Feature Decomposition and Attribute Augmentation for Attribu...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Wang, Xin Liu, Fangfang Guo, Caili Li, Zheng Zhang, Hao Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350359855

The attribute-based person search task aims to find matching pedestrian images by text attributes, which is relevant in scenarios where no query image is given. However, the existing methods exhibit inferior performance due to their inadequate representation of local fine-grained features, which hinders their ability to effectively model intra-class variations. In addition, there is a zero-shot retrieval problem due to the large number of unseen categories in the test set, resulting in suboptimal generalization performance. In this paper, we propose a novel Feature Decomposition and Attribute Augmentation (FDAA) framework to solve the above problems. Firstly, by decomposing the global features of the image from different directions, the local features are extracted at multiple granularities, thus effectively improving the discrimination ability of the model. Secondly, an attribute augmentation strategy is proposed that can effectively expand the combination of attributes during training and improve the generalization ability of the model. Extensive experiments on the PETA, Market-1501 Attribute, and PA100K datasets demonstrate the effectiveness of our proposed method, outperforming state-of-the-art methods. © 2023 IEEE.

关键词： image enhancement

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Multiscale Vision Transformer with Deep Clustering-Guided Refinement for Weakly Supervised Object Localization

Multiscale Vision Transformer with Deep Clustering-Guided Re...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Kim, David Minkwan Cha, Sinhae Kang, Byeongkeun Hanyang University Department of Computer Science Seoul Korea Republic of Seoul National University of Science and Technology Department of Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

This work addresses the task of weakly-supervised object localization. The goal is to learn object localization using only image-level class labels, which are much easier to obtain compared to bounding box annotations. This task is important because it reduces the need for labor-intensive ground-Truth annotations. However, methods for object localization trained using weak supervision often suffer from limited accuracy in localization. To address this challenge and enhance localization accuracy, we propose a multiscale object localization transformer (MOLT). It comprises multiple object localization transformers that extract patch embeddings across various scales. Moreover, we introduce a deep clustering-guided refinement method that further enhances localization accuracy by utilizing separately extracted image segments. These segments are obtained by clustering pixels using convolutional neural networks. Finally, we demonstrate the effectiveness of our proposed method by conducting experiments on the publicly available ILSVRC-2012 dataset. © 2023 IEEE.

关键词： image enhancement

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Foreground-Background Disentanglement based on image and Feature Co-Learning for 3D-Aware Generative Models

Foreground-Background Disentanglement based on Image and Fea...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Lee, Sanghyuk Kim, Daeha Song, Byung Cheol Inha University Department of Electrical and Computer Engineering Incheon Korea Republic of

ISBN: (纸本)9798350359855

Recently, studies on generative models using 3D information are active. GIRAFFE, one of the latest 3D-aware generative models, shows better feature disentanglement than existing generative models because it generates an image through volume rendering of independently formed 3D neural feature fields. However, GIRAFFE still suffers from an issue where foreground and background disentanglement is not smooth. In order to accomplish better disentanglement performance than GIRAFFE, we propose co-adversarial learning of the generative model at both image- and feature-levels. As a result of rich simulation experiments, the proposed generative model can produce photo-realistic images with only fewer parameters than existing 3D-aware generative models, along with excellent foreground-background disentanglement performance. © 2023 IEEE.

关键词： Volume rendering

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Enhancing image Denoising Performance through a Family of Algorithms 2

Enhancing Image Denoising Performance through a Family of Al...

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2nd International conference on Paradigm Shifts in communications Embedded Systems, Machine Learning and Signal processing, PCEMS 2023

作者： Gupta, Omkar Kalapala, Irika Nikhita Bhurchandi, Kishor M. Visvesvaraya National Institute of Technology Department of Electronics and Communication Engineering Nagpur India

ISBN: (纸本)9798350310719

images are an integral and indispensable aspect of various disciplines, such as medicine, surveillance, and the entertainment industry. However, the quality of images can be severely compromised by the presence of sensor noise, quantization errors, or transmission errors. This research proposes a novel approach that combines wavelet thresholding and BM3D (Block-Matching and 3D Filtering) techniques for effective image *** efficacy of the methodologies is evaluated and compared to cutting-edge denoising techniques, demonstrating superior performance in both quantitative metrics and visual quality. Furthermore, the study delves into the intricate mechanisms underlying the denoising process and the impact of various parameters on the denoising performance, contributing significantly to the field of image denoising. © 2023 IEEE.

关键词： image denoising

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A Comprehensive Review of image Encryption Algorithms: Techniques and Applications 5

A Comprehensive Review of Image Encryption Algorithms: Techn...

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5th International conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sheeba Joice, C. Hitha Shanthini, S. Harsha, K. Gopika, S. Saveetha Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

The necessity of secure image transmission and storage has become more urgent in the digital era. In a variety of applications, including medical imaging, military communications, and personal data protection, image encryption is essential for safeguarding sensitive visual data from unauthorized access and assuring privacy. This article offers a thorough examination of the current image encryption algorithms, with an emphasis on their strengths, weaknesses, and methodologies. Classical methods, such as symmetric and asymmetric encryption, as well as more sophisticated techniques, such as chaos-based, quantum-based, and hybrid encryption methods, are investigated. The objective of this review is to offer researchers and practitioners a valuable resource for comprehending the current state of image encryption and directing the development of more efficient and robust encryption solutions. © 2024 IEEE.

关键词： Military communication visualization Reviews image processing image communication Data protection Data transfer Encryption image storage Biomedical imaging

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Chest X-ray image Super-Resolution via Deep Contrast Consistent Feature Network 5th

Chest X-ray Image Super-Resolution via Deep Contrast Consist...

引用

5th International conference on Recent Trends in image processing and Pattern Recognition (RTIP2R)

作者： Greeshma, M. S. Bindu, V. R. Mahatma Gandhi Univ Sch Comp Sci Kottayam Kerala India

ISBN: (纸本)9783031235986;9783031235993

This paper proposes a chest X-ray image super-resolution reconstruction method - Deep Contrast Consistent Feature Network, which articulates the contrast consistency as a task to learn spatial contrast enhancement curve followed by a depth contrast network. The proposed method trains a network with visual quality measures through learning non-reference loss - Contrast Consistency Loss, which aims to overcome contrast overstretching and contrastive variation. Specifically, this network does not need to reference images at the time of loss formulation. We evaluated the proposed method for chest X-ray 5Kdatasets over several benchmarks metrics and models for quantitative and qualitative analysis. The extensive experiments report that the proposed model outperforms other approaches.

关键词： Super resolution Chest X-ray image Deep network Contrast enhancement

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