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检索条件"任意字段=2024 Conference on Visual Communications and Image Processing"

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A Text-Oriented Transformer with an image Aesthetics Assessment Fusion Network for visual-Textual Sentiment Analysis 39th

A Text-Oriented Transformer with an Image Aesthetics Assessm...

引用

39th CCF National conference on Computer Applications

作者： Liu, Ziyu Zhang, Zhonglin Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9789819796700;9789819796717

The rapid advancement of social networks has significantly altered how people convey their emotions, increasingly through a mix of images and text on social media platforms. visual-textual sentiment analysis has garnered considerable attention because it incorporates visual data into textual sentiment analysis. Moreover, most current visual-textual sentiment analysis approaches underperform because of their limited exploitation of the correlations between these two modalities. Furthermore, current methods for visual analysis tend to focus excessively on extracting image features while neglecting the aesthetic aspects of images. To address these issues, this study introduces a text-oriented transformer with an image aesthetics assessment fusion network mechanism, ter633133_1_En_12_Chaptermed ToTIAN. This approach comprises two main components: aesthetics-oriented visual feature extraction and a text-oriented transformer. It integrates textual information with image aesthetics-which include emotional cues-via a multiattention mechanism, resulting in a comprehensive representation enriched with emotional cues. Extensive experiments on two publicly available datasets confirmed the superior efficacy of the proposed ToTIAN approach compared with the prevalent unimodal and multimodal methods.

关键词： Text orientation image aesthetics assessment visual-textual sentiment analysis

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HRC-Net : A High Robust ConvNeXt for Noisy image 16

HRC-Net : A High Robust ConvNeXt for Noisy Image

引用

16th International conference on Wireless communications and Signal processing, WCSP 2024

作者： Hu, Chengfeng Zhu, Jun Yang, Yang School of Electronic Information Engineering Anhui University Hefei230031 China

ISBN: (纸本)9798350390643

Despite the fairly good performance of Convolutional Neural Networks (CNNs) in image classification tasks, existing CNNs do not perform well when handling datasets with Gaussian noise. This results in the instability of model performance under extreme conditions or due to device aging. To address this issue, this paper introduces parameter-free attention and employs a simple wavelet decomposition and reconstruction method for image preprocessing, which minimizes the impact of noise on the model's performance. When the noise ratio is 40%, the accuracy of the improved model is approximately 90.4%, representing an increase of 3.1% compared to the original model's accuracy of 87.3%. This indicates that the proposed model in this paper is less sensitive to the impact of Gaussian noise, demonstrating its high robustness. © 2024 IEEE.

关键词： Wavelet decomposition

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The Effects of Infrared Sensor Wavelength on Panchromatic image Colorization Performance 32

The Effects of Infrared Sensor Wavelength on Panchromatic Im...

引用

32nd IEEE Signal processing and communications Applications conference (SIU)

作者： Akin, Suleyman Emir Akgun, Toygar TOBB Ekon & Teknol Univ Bilgisayar Muhendisligi Bolumu Ankara Turkiye

ISBN: (纸本)9798350388978;9798350388961

Infrared (IR) imaging sensors, designed to detect the wavelength range between 0.9 mu m and 14 mu m, offer unique advantages over daylight cameras in consumer, industrial, and defense applications. However, IR images lack natural color information and can be challenging for individuals without sensor-specific training to interpret. Consequently, transforming IR images into perceptually realistic color images represents a valuable research endeavor with significant commercial potential. Recently, various studies utilizing deep neural networks for colorizing single-mode (near-IR or thermal) infrared images have been reported. This article will apply a common neural network architecture to images captured with different imaging modes (near-IR, thermal IR, and low-light) for colorization and compare the results. These experiments will examine the influence of perceived wavelength on the colorization process.

关键词： infrared thermal colorization CNN autoencoder

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Real-time Learned image Codec on FPGA

Real-time Learned Image Codec on FPGA

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Sun, Heming Yi, Qingyang Lin, Fangzheng Yu, Lu Katto, Jiro Fujita, Masahiro Waseda Univ Tokyo Japan Zhejiang Univ Hangzhou Zhejiang Peoples R China JST PRESTO Kawaguchi Saitama Japan Univ Tokyo Tokyo Japan AIST Tsukuba Ibaraki Japan

This demo paper gives a real-time learned image codec on FPGA. By using Xilinx VCU128, the proposed system reaches 720P@30fps codec, which is 7.76x faster than prior work.

ISBN: (纸本)9781665475921

This demo paper gives a real-time learned image codec on FPGA. By using Xilinx VCU128, the proposed system reaches 720P@30fps codec, which is 7.76x faster than prior work.

关键词： image coding neural network FPGA

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A Low-Light-Level image Enhancement Algorithm Combining Retinex and Transformer

A Low-Light-Level Image Enhancement Algorithm Combining Reti...

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2024 International conference on Remote Sensing, Mapping, and image processing, RSMIP 2024

作者： Zhao, Xinpu Li, Liang Chongqing University of Technology College of Computer Science and Engineering Chongqing Banan China

ISBN: (纸本)9781510680012

The Low Light image Enhancement (LLIE) task aims to restore images with poor lighting conditions and visual effects to images with good lighting conditions and visual effects. However, the enhancement results output by existing methods always include various image degradation such as overexposure, artifacts, and color shift. To alleviate these issues, a low light image enhancement method based on Retinex theory and Transformer is proposed. Designed a dual branch network architecture with Transformer blocks. One branch is used to enhance local details of the image, while the other branch is used to adjust the color and brightness of the enhancement results. The dataset used in the experiment was the LOL dataset, which was 23.42 and 0.81 in the PSNR and SSIM indicators, respectively. Compared with the suboptimal algorithm, it improved by 1.06 and 0.016, respectively. The experimental results show that this method has achieved excellent performance in low light image enhancement tasks and has certain application value. © 2024 SPIE.

关键词： image enhancement

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EFFICIENT SCENE TEXT image SUPER-RESOLUTION WITH SEMANTIC GUIDANCE 49

EFFICIENT SCENE TEXT IMAGE SUPER-RESOLUTION WITH SEMANTIC GU...

引用

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

作者： TomyEnrique, Leowu Du, Xiangcheng Liu, Kangliang Yuan, Han Zhou, Zhao Jin, Cheng Fudan Univ Sch Comp Sci Shanghai Peoples R China Videt Technol Shanghai Peoples R China MCT Innovat Ctr Callig & Painting Creat Technol Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Scene text image super-resolution has significantly improved the accuracy of scene text recognition. However, many existing methods emphasize performance over efficiency and ignore the practical need for lightweight solutions in deployment scenarios. Faced with the issues, our work proposes an efficient framework called SGENet to facilitate deployment on resource-limited platforms. SGENet contains two branches: super-resolution branch and semantic guidance branch. We apply a lightweight pre-trained recognizer as a semantic extractor to enhance the understanding of text information. Meanwhile, we design the visual-semantic alignment module to achieve bidirectional alignment between image features and semantics, resulting in the generation of high-quality prior guidance. We conduct extensive experiments on benchmark dataset, and the proposed SGENet achieves excellent performance with fewer computational costs.

关键词： Scene text image super-resolution efficient model semantic guidance

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An Approach for Enhancement of Low Contrast Gray Scale image Using Fuzzy Logic and Sigmoid Function 2

An Approach for Enhancement of Low Contrast Gray Scale Image...

引用

2nd IEEE International conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Kumar, Abhishek Kumar, Sanjeev Sarala Birla University Department of Electronics & Communication Ranchi India

ISBN: (纸本)9798350372847

Contrast enhancement plays a pivotal role in image processing, particularly for improving the visual quality of images in various applications. This paper presents an approach for enhancing such images by employing a point processing with the combination of Gaussian membership function for fuzzification and sigmoid function for modification. First, the image is fuzzified using Gaussian membership functions to transform pixel intensities into fuzzy sets, allowing for a more robust representation of the image's underlying characteristics. This fuzzification process effectively captures the uncertainty associated with pixel values in low contrast regions. Subsequently, a sigmoid function is applied to the fuzzified image to modify the pixel intensities. Experimental results demonstrate the efficacy of the proposed method in enhancing low contrast images, as evidenced by significant improvements in visual quality and quantitative metrics such as peak signal-to-noise ratio (PSNR) and entropy. The proposed approach offers a promising solution for enhancing low contrast images across various applications, contributing to advancements in image processing and analysis. © 2024 IEEE.

关键词： Fuzzy logic

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Research on Traditional Art image Reconstruction Method Based on Computer Vision Analysis 3

Research on Traditional Art Image Reconstruction Method Base...

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3rd International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Ruan, Xunmiao Guangdong University of Science &Technology Dongguan Guangdong523083 China

ISBN: (纸本)9798350376173

In the information age, image processing technology has become prevalent across various domains. To enhance image correction, computer vision algorithms can be employed. Traditional methods for structural system identification rely on wired or wireless measurement systems for data gathering, which can be cumbersome and time-consuming due to the need for arranging sensors and data collection equipment. The proposed system involves two primary steps: line drawing extraction and rendering. During the extraction phase, an edge detection technique that integrates low and middle-level visual information is utilized. Based on the results of traditional edge detection, the edges are simplified and connected using computer vision principles. This approach treats image processing as the focal point and aims to establish a scientific model for distorted images. image restoration can be facilitated through sample image filtering and noise reduction techniques. Additionally, this article delves into the principles and associated technologies of computer vision, analyzing patterns of visual interaction. Chinese traditional art imagery, as a cultural medium and symbol, fulfills an artistic communication function while preserving the nation's rich cultural heritage. The image processing techniques grounded in computer vision algorithms offer numerous advantages and superior accuracy. © 2024 IEEE.

关键词： Noise abatement

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ENHANCED DETECTION OF SMALL OBJECTS IN AERIAL imageRY: A HIGH-RESOLUTION NEURAL NETWORK APPROACH WITH AMPLIFIED FEATURE PYRAMID AND SIGMOID RE-WEIGHTING 31

ENHANCED DETECTION OF SMALL OBJECTS IN AERIAL IMAGERY: A HIG...

引用

2024 International conference on image processing

作者： Park, Chanyeong Jang, Junbo Kim, Heegwang Paik, Joonki Chung Ang Univ Dept Image Seoul South Korea Chung Ang Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350349405;9798350349399

Detecting small objects in drone-captured images or aerial videos is challenging due to their minimal representation. As data traverses deep learning networks, the information about small objects can diminish, making high-resolution images essential for enhanced detection performance. However, high-resolution images increase computational load undesirably. Leveraging this fact, we propose a streamlined neural network designed specifically for small object detection in high-resolution images. The proposed network encompasses three main components: i) Enhanced High-Resolution processing Module (EHRPM), ii) the Small Object Feature Amplified Feature Pyramid Network (SOFA-FPN) with its Edge Enhancement Module (EEM), Cross Lateral Connection Module (CLCM), and Dual Bottom-up Convolution Module (DBCM), and iii) the Sigmoid Re-weighting Module (SRM). Compared to several state-of-the-art networks, our method delivers superior performance with fewer parameters and a lower computational demand. The source code is available at https://***/datu0615/EHRPM.

关键词： Small Object Detection Aerial images Multi-scale Feature Fusion High-resolution processing

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WAVELET-GUIDED ACCELERATION OF TEXT INVERSION IN DIFFUSION-BASED image EDITING 49

WAVELET-GUIDED ACCELERATION OF TEXT INVERSION IN DIFFUSION-B...

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

作者： Koo, Gwanhyeong Yoon, Sunjae Yoo, Chang D. Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (纸本)9798350344868;9798350344851

In the field of image editing, Null-text Inversion (NTI) enables fine-grained editing while preserving the structure of the original image by optimizing null embeddings during the DDIM sampling process. However, the NTI process is time-consuming, taking more than two minutes per image. To address this, we introduce an innovative method that maintains the principles of the NTI while accelerating the image editing process. We propose the WaveOpt-Estimator, which determines the text optimization endpoint based on frequency characteristics. Utilizing wavelet transform analysis to identify the image's frequency characteristics, we can limit text optimization to specific timesteps during the DDIM sampling process. By adopting the Negative-Prompt Inversion (NPI) concept, a target prompt representing the original image serves as the initial text value for optimization. This approach maintains performance comparable to NTI while reducing the average editing time by over 80% compared to the NTI method. Our method presents a promising approach for efficient, high-quality image editing based on diffusion models.

关键词： image editing Null-Text Inversion text optimization diffusion model

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