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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Fourier residuals image deblurring network based on deep learning 43

Fourier residuals image deblurring network based on deep lea...

引用

43rd Chinese Control conference, CCC 2024

作者： Wu, Ziyang Wang, Xuanhong Zhang, Dou Li, Gang xiao, Yun Xi'an University of Posts and Telecommunications School of Telecommunication and Information Engineering Xi'an710121 China Northwest University School of Information Science and Technology Shaanxi 710127 China

ISBN: (纸本)9789887581581

Blurry images not only affect people's visual perception but also adversely affect autonomous driving systems, medical imaging, object recognition, and other fields. With the rapid development of the CNN has become one of the research hotspots due to its good performance in the field of image deblurring. The existing deblurring model based on deep learning has low parameters, fast running time, and good detail processing of blurry images, but the accuracy of the deblurring image from the global perspective needs to be improved. To solve this problem, Fourier Residual GELU Network (FRGNet) is proposed in this paper. FRGNet introduces a new feature extraction module to improve the feature extraction capability of blury images. This module uses atrous convolution to increase the receptive field. The standard ResBlock is modified and the Layer Normalization is added so that the network can be smoothly trained when the learning rate is large. The nonlinear activation function GELU is applied in the frequency domain of blurry images, which can bring the global context learning ability to the network. Also added is a coordinated attention module to build information and build an improved ResBlock. Using the information of the image blurry kernel, the pixel-level difference between a blurry image and a sharp image is learned from the frequency domain. Comprehensive experiments show that this method improves the deblurring performance. Its PSNR value in GOPRO is 33.22dB, which is 0.4dB higher than DeepRFT. The PSNR value in the RealBlur-R is 40.16dB, which is 0.36dB higher than DeepRFT. Further ablation studies showed the effectiveness of the various components in our proposed model. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Frequency domain analysis

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Analysis of Optimization on Sparse Matrix Vector Multiplication Model Application in Digital Signal processing 3

Analysis of Optimization on Sparse Matrix Vector Multiplicat...

引用

3rd IEEE International conference on Electrical Power and Energy systems, ICEPES 2024

作者： Shaji, Alden C Khare, Kavita Maulana Azad National Institute of Technology Electronics & Communication Engineering Department Bhopal India

ISBN: (纸本)9798350390728

Conventional digital signal processing (DSP) technology, utilizing DSP and FPGA, excels in real-time signal processing but faces limitations in handling large-scale data and achieving high frequency resolution. This restricts its effectiveness for tasks involving offline data processing, analysis, and mining in applications with extensive data volumes. Some of the SpMV applications in image editing operations like linear filtering, resizing and mixing are reformulated in the SpMV multiplication form. The growing complexity of applications and the demand for top-notch digital data necessitate a boost in processing power. With energy efficiency becoming a critical concern, there is research on designing SpMV algorithms that minimize energy consumption. This is particularly relevant for embedded DSP systems and applications where power efficiency is a key consideration. A comparative study of High-Level Synthesis (HLS) techniques to reduce the power consumption and resource utilization conducted on xilinx-Zynq 7000 FPGA is presented. This paper explores various HLS pragmas techniques using OpenCL framework applied on Sparse Matrix Vector Multiplication (SpMV) kernel for optimizing power efficiency and resource utilization. HLS has emerged has a pivotal technology for simplifying the design process of complex digital systems by allowing developers to work at higher levels of abstraction, using high-level programming languages. From the analysis, identified the best optimization technique for the low power and the minimum resource utilization for the SpMV implementation on FPGA hence it can be more useful for applications in the field of CNN. © 2024 IEEE.

关键词： High level synthesis

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Research on Gaussian Mixture Computational Learning Mode Based on MOOC Online Education 5th

Research on Gaussian Mixture Computational Learning Mode Bas...

引用

5th International conference on Real Time Intelligent systems, RTIS 2023

作者： Liu, xi Liu, Cheng Hao, He Department of Academic Affairs Southwest Jiaotong University Hope College Sichuan Chengdu610000 China

ISBN: (纸本)9783031558474

Gaussian Mixture Model (GMM) has been widely applied in various disciplines such as model recognition, computer visualization, machine learning, digital elevation, and life sciences, and can effectively solve complex mathematical problems such as image processing, clustering, computation of probability density functions, and optimization of modeling. With the emergence of "Internet + Education," blended learning has gained widespread attention globally and has achieved significant success in many academic institutions. Therefore, in today’s society, many scholars are exploring utilising the Gaussian Mixture Learning System in MOOCs to promote educational development and achieve favorable outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Learning systems

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CLIPCEIL: Domain Generalization through CLIP via Channel rEfinement and image-text aLignment 38

CLIPCEIL: Domain Generalization through CLIP via Channel rEf...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Yu, xi Yoo, Shinjae Lin, Yuewei Artificial Intelligence Department Computing and Data Science United States Directorate Brookhaven National Laboratory UptonNY11973 United States

Domain generalization (DG) is a fundamental yet challenging topic in machine learning. Recently, the remarkable zero-shot capabilities of the large pre-trained vision-language model (e.g., CLIP) have made it popular for various downstream tasks. However, the effectiveness of this capacity often degrades when there are shifts in data distribution during testing compared to the training data. In this paper, we propose a novel method, known as CLIPCEIL, a model that utilizes Channel rEfinement and image-text aLignment to facilitate the CLIP to the inaccessible out-of-distribution test datasets that exhibit domain shifts. Specifically, we refine the feature channels in the visual domain to ensure they contain domain-invariant and class-relevant features by using a lightweight adapter. This is achieved by minimizing the inter-domain variance while maximizing the inter-class variance. In the meantime, we ensure the image-text alignment by aligning text embeddings of the class descriptions and their corresponding image embedding while further removing the domain-specific features. Moreover, our model integrates multi-scale CLIP features by utilizing a self-attention fusion module, technically implemented through one Transformer layer. Extensive experiments on five widely used benchmark datasets demonstrate that CLIPCEIL outperforms the existing state-of-the-art methods. The source code is available at https://***/yuxi120407/CLIPCEIL. © 2024 Neural information processing systems foundation. All rights reserved.

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A Lightweight Human Pose Estimation Algorithm Based on High Resolution Network 5

A Lightweight Human Pose Estimation Algorithm Based on High ...

引用

5th International conference on Natural Language processing, ICNLP 2023

作者： Ma, Sai Ge, Haibo He, Wenhao Huang, Chaofeng An, Yu Zhou, Ting Xi'an University of Posts and Telecommunication School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350302219

Human pose estimation is an important research direction in the field of computer vision. At present, the mainstream human pose estimation algorithms have high complexity, large amount of calculation, and cannot be run on resource-constrained devices such as mobile terminals, which severely limits the popularization and application of this technology. Aiming at the problem of increased network model parameters and computational complexity, based on the High-Resolution Network (HRNet), a lightweight human pose estimation network incorporating Ghost module and attention mechanism is proposed. Replaced with Ghost convolution, and added the attention mechanism Concurrent Spatial and Channel Squeeze and Channel Excitation Net module on this basis to ensure the prediction accuracy of the network. Under the same image resolution and environment configuration, the experimental results on the COCO dataset show that the improved network model reduces the number of parameters by 98.3% compared to the high-resolution network model, and reduces the computational complexity by 67.6%. The experimental results show that the improved network model can effectively reduce the amount of network parameters and reduce the computational complexity while maintaining a certain prediction accuracy. © 2023 IEEE.

关键词： Computational complexity

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image processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm 8

Image Processing Implementation to Classify Coffee Fruit Rip...

引用

8th International conference on Information Management and Technology, ICIMTech 2023

作者： Hussaini Dermawan, M. Farhan Witarsyah, Deden Syamsuar, Dedy Fakhrurroja, Hanif Luthfi, Ahmad Herdiansyah, Muhammad Izman Telkom University Faculty of Industrial Engineering Bandung Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Islamic University of Indonesia Faculty of Industrial Technology Department of Informatics Jogjakarta Indonesia Universitas Bina Darma Informatics Study Program Faculty of Science and Technology Palembang Indonesia

ISBN: (纸本)9798350326093

Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is because the determination is made subjectively by coffee farmers. As for the classification of coffee fruit maturity levels automatically, it can be faster with objective determination, therefore the use of image processing is relatively easier, faster, and based on a quantified descriptive assessment to determine coffee maturity. image processing is a method used to process or manipulate images in 2-dimensional form. In the classification process, there are many methods used to obtain classification of objects based on training data. One of the algorithms used for the classification process is K-Nearest Neighbor (KNN). KNN is a classification technique for objects based on training data that is the closest or has similar characteristics to the object. KNN includes supervised learning algorithms, where the results of the new query instance are classified based on the majority of the categories in K-Nearest Neighbors (K-NN). The finding indicated that class classification of ripe and unripe were 88,24 % and 100% respectively with 93,33% accuracy level. © 2023 IEEE.

关键词： Classification (of information)

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image Understanding Through Visual Question Answering: A Review from Past Research 23th

Image Understanding Through Visual Question Answering: A Rev...

引用

23rd International conference on Intelligent systems Design and Applications, ISDA 2023

作者： Yanda, Nagamani Tagore Babu, J. Aswin Kumar, K. Taraka Rama Rao, M. Ranjith Varma, K.V. Rahul Babu, N. GMR Institute of Technology Rajam532127 India

ISBN: (纸本)9783031648465

Visual Question Answering (VQA) lies at the crossroads of computer vision, natural language processing, and deep learning, captivating researchers across various AI domains. This dynamic field involves processing an image alongside a corresponding textual question, generating, or selecting an answer from provided options. The past five years have witnessed substantial advancements in VQA and visual reasoning, fueled by deep learning and extensive annotated datasets. This study presents a comprehensive literature review, delving into the current state-of-the-art from four perspectives: problem definition, existing datasets, literature review, and evaluation metrics. Through a critical analysis, we address dataset limitations and scrutinize contemporary algorithms. Here we use multimodal Fusion, which achieves the state of the art compared to existing methodologies. Moreover, we explore potential future research directions to inspire innovative solutions and applications in this evolving domain, aiming to propel VQA into new realms of exploration and practical utility. This project will allow users to input an image and image-related text so that it will aid the question-answering system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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A Comparative Analysis Between the Performance of the Extracted Features of JPEG and PNG on a Raspberry Pi Iris Recognition System 38

A Comparative Analysis Between the Performance of the Extrac...

引用

38th IEEE Region 10 conference, TENCON 2023

作者： Martinez, Gio Arturo S. Moralde, Reese Elijah B. Linsangan, Noel B. Ang, Rayellee Myrtle Laire School of Electrical Electronics and Computer Engineering Mapua University Intramuros Manila Philippines

ISBN: (纸本)9798350302196

The image file format used in iris recognition systems has been proven to affect the performance of iris authentication. PNG and JPEG, the image file formats that are among the most common, will be compared in this study by converting a single iris image to the two image file formats, which will then be evaluated using total and average processing time FRR and FAR. The two file formats differ in their used image compression algorithms. JPEG achieves a lossy image compression, which results in irreversible data loss after every compression, using DCT (Discrete Cosine Transform). On the other hand, PNG achieves lossless image compression using the DEFLATE algorithm. Both algorithms' motive is to compress data. The latter, however, executes this without losing data. The image file formats are compared on a Raspberry Pi 4B that runs the iris recognition system utilizing Daugman's algorithm. After conducting 80 trials of iris recognition in the system, the JPEG database resulted in a total and average processing time of 70.8301s and 1.7708s respectively, and a FRR of 0.05 and a FAR of 0.05. Meanwhile, the PNG database yielded a total and average processing time of 72.2576s and 1.8064s respectively, and a FRR of 0.10 and a FAR of 0.05. This comparative study identified that the extracted iris features from JPEG produced better recognition performance results in the implemented iris recognition system over PNG. © 2023 IEEE.

关键词： image compression

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PM2.5 Prediction Based on PCA-EDWaveNet-LSTM 2

PM2.5 Prediction Based on PCA-EDWaveNet-LSTM

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2nd International conference on Advanced algorithms and Signal image processing, AASIP 2022

作者： Zhou, xin Zhu, Li Jin, Wenping Luo, Ting Software College Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9781510660571

At this stage, the PM2.5 concentration prediction algorithm ignores the influence of other air pollution factors, and has not realized the time-dependent integration with the influence of other environmental pollutants. In this regard, the PCAEDWavenet-LSTM algorithm considering other air pollution characteristics is proposed. The algorithm proposes to consider other air pollution factors, combine the influence of other air pollution factors with the times dependence on PM2.5 particle concentration, and establish a PCA-EDWaveNet-LSTM algorithm based on air pollution characteristics. In the empirical analysis of PM2.5 historical concentration prediction in xi'an, the algorithm is compared with RF_Regression algorithm, SVM algorithm, and LSTM neural network. The results show that the prediction performance of this algorithm is better than various traditional prediction algorithms in PM2.5 concentration prediction. © 2022 SPIE.

关键词： Forecasting

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Conditional Controllable image Fusion 38

Conditional Controllable Image Fusion

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Cao, Bing Xu, xingxin Zhu, Pengfei Wang, Qilong Hu, Qinghua College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China School of New Media and Communication Tianjin University Tianjin China

image fusion aims to integrate complementary information from multiple input images acquired through various sources to synthesize a new fused image. Existing methods usually employ distinct constraint designs tailored to specific scenes, forming fixed fusion paradigms. However, this data-driven fusion approach is challenging to deploy in varying scenarios, especially in rapidly changing environments. To address this issue, we propose a conditional controllable fusion (CCF) framework for general image fusion tasks without specific training. Due to the dynamic differences of different samples, our CCF employs specific fusion constraints for each individual in practice. Given the powerful generative capabilities of the denoising diffusion model, we first inject the specific constraints into the pre-trained DDPM as adaptive fusion conditions. The appropriate conditions are dynamically selected to ensure the fusion process remains responsive to the specific requirements in each reverse diffusion stage. Thus, CCF enables conditionally calibrating the fused images step by step. Extensive experiments validate our effectiveness in general fusion tasks across diverse scenarios against the competing methods without additional training. The code is publicly available. © 2024 Neural information processing systems foundation. All rights reserved.

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