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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4813 条记录，以下是371-380 订阅

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Chambolle's Projection-based Total Variation Deepnet Layer for image Denoising 19

Chambolle's Projection-based Total Variation Deepnet Layer f...

引用

19th ieee international conference on Intelligent computer Communication and processing conference, ICCP 2023

作者： Grosu, George Florin Terebes, Romulus Technical University of Cluj-Napoca Communications Department Cluj-Napoca Romania

ISBN: (纸本)9798350370355

This research paper presents the implementation and evaluation of a Total Variation (TV) layer within a deep learning framework for image denoising tasks. The TV layer is based on Chambolle's projection method and aims to enhance the performance of a baseline denoising network. The study investigates the effectiveness of incorporating a single TV denoising layer, which adds only six parameters to an arbitrary baseline autoencoder. The resulting models are evaluated across three datasets with different Gaussian noise. By comparing an arbitrary network architecture with an identical counterpart model that incorporates the TV denoising layer, the results indicate significant improvements in performance. The magnitude of these performance gains diminishes as the variance of noise increases. Notably, for lower Gaussian noise variance such as for a standard deviation of 25, the gain in terms of the mean peak signal-to-noise ratio (PSNR) metric is estimated to be approximately +0.89 dB, a discernible improvement that is visually perceptible. Although the resulting model has no significant increase in terms of model size, it does present significant increases in complexity. © 2023 ieee.

关键词： image denoising

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Privacy-Preserving, Low-Storage, and High-Quality image processing in IoT and Clouds

Privacy-Preserving, Low-Storage, and High-Quality Image Proc...

引用

2023 international conference on Intelligent Communication and computer Engineering, ICICCE 2023

作者： Zhao, Shushan Chiang, Wes Central Connecticut State University Department of Computer Electronics & Graphics Technology New BritainCT United States International Business & Strategy Brock University Department of Marketing St. CatharinesON Canada

ISBN: (纸本)9798350395617

In Internet of Things (IoT) and clouds, while many image processing tasks are outsourced to third party cloud computing platforms, image processing in encrypted domain is needed in many services for data confidentiality and privacy-preserving purposes. There are many solutions for image scaling, cropping and color correction in this context. However, most of the solutions are not compatible with each other and there are few solutions supporting all these operations. In this paper, we propose a scheme that supports image scaling, cropping, and color correction in encrypted domain with minimum storage overhead. An optional improvement solution is added for a conditional quality issue in certain circumstances. Analysis shows storage requirement is drastically reduced while performance otherwise remains unchanged. © 2023 ieee.

关键词： Internet of things

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Comparison of Deep Learning Models for Automatic image Descriptors 20

Comparison of Deep Learning Models for Automatic Image Descr...

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20th ieee India Council international conference, INDICON 2023

作者： Agarwal, Lakshita Verma, Bindu Delhi Technological University Department of Information Technology Delhi India

ISBN: (纸本)9798350305593

image description is a task which combines the methods like Natural Language processing, Artificial Intelligence and computer Vision, which aims to generate contextually and semantically correct descriptions for an image dataset. The majority of the most recent methods rely on neural networks for their execution. Different Recurrent Neural Network (RNN) models are utilised to construct models for the language, and different already developed Convolutional Neural Network (CNN) models are employed for features extraction. In this paper, the main focus is on the most widely used datasets, approaches, benchmarks and the evaluation metrics for generating image descriptions. The paper involves a comparative analysis of various models for generating the descriptions and the results obtained on the Flickr8k dataset has been analyzed for them. In addition, the advantages and disadvantages of these models are being investigated. © 2023 ieee.

关键词： Automatic Learning computer Vision Deep Learning image Descriptions Natural Language processing

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Punisher: A Deep Reinforcement Learning Model Trained by Correcting Bad Actions 2

Punisher: A Deep Reinforcement Learning Model Trained by Cor...

引用

2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Yang, Jianyi International Technological University Computer Science Department Santa ClaraCA95050 United States

ISBN: (纸本)9798350331417

Deep Reinforcement Learning (DRL) is a deep learning (DL) network model that uses environmental feedback to train and make decisions. Expected value, as a powerful mathematical tool, is widely used in DRL network training. However, there are often deviations between the expected values and the actual values obtained from the environment. Additionally, accumulative deviations are also present in DRL networks. The accumulation of these deviations can result in a slower training speed and negatively impact the network's stability. To address these issues, this paper proposes a new DRL training method called Punisher. The principle behind Punisher is to identify the bad actions made by the DRL agent and correct only those actions during the training process. By focusing on correcting the bad actions, Punisher aims to improve the overall performance and stability of the DRL network. The experimental results demonstrate that the Punisher method exhibits excellent performance, faster training speeds and greater network stability, making it a promising approach for efficiently training DRL agents in various applications. Experiments presented in the main content of this paper have been posted at GitHub at https://***/Jimmyoungyi/Punisher. © 2023 ieee.

关键词： deep learning punisher reinforcement learning

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Bi-Cubic Interpolation image Scaling Algorithm Based on FPGA Implementation 3

Bi-Cubic Interpolation Image Scaling Algorithm Based on FPGA...

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3rd international conference on computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Zhou, Zeng Wang, Yan Xu, Conghao Zhang, Yang University of South China College of Electrical Engineering Hengyang Hunan China

ISBN: (纸本)9798350372052

image resizing plays a very important role in image processing, offering good scalability and extensibility. Currently, there are some issues with existing scaling interpolation algorithms, such as complex hardware implementation, high resource consumption, long operation times, and poor realtime performance. To address these challenges, this paper introduces a design method based on the image bicubic interpolation scaling algorithm and optimizes the floating-point operation aspect of image processing. Utilizing the FPGA platform, a step-by-step scaling approach is employed, first performing vertical scaling and then horizontal scaling to achieve parallel processing. This design significantly improves scaling efficiency, and its feasibility is demonstrated through functional simulation. © 2023 ieee.

关键词： Field programmable gate arrays (FPGA)

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Enhancing Scene Text Segmentation through Subtask Decomposition 2

Enhancing Scene Text Segmentation through Subtask Decomposit...

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2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Wang, Yong Chen, Youguang East China Normal University School of Data Science and Engineering Shanghai China

ISBN: (纸本)9798350331417

The field of image processing widely utilizes scene text segmentation technology, with applications extending to image editing and font style transfer. These applications enhance image understanding quality and aid in boosting the performance of numerous computer vision tasks. The advent and progression of deep learning have led to substantial advancements in scene text segmentation technology. However, the limited size of existing scene text segmentation datasets constrains the performance of models. Therefore, we propose an algorithm for synthetic segmentation data. We first pretrain the model using large-scale synthetic data, then fine-tune it on the target dataset to address the issue of limited dataset size. Existing models employ end-to-end segmentation, which presents challenges in segmentation. We propose a scene text segmentation method. By decomposing the segmentation task into subtasks and solving them one by one, the complexity of the task can be reduced compared to direct segmentation of the entire image significantly improving the segmentation effect. The proposed method consists of three modules: a fragment crop module, a fragment segmentation module, and a fragment combination module. The fragment crop module is composed of an additional corp layer added after DBnet. The fragment segmentation module can be embedded with various segmentation methods. The fragment combination module uses the maximum pixel value pasting algorithm to combine the segmented fragments. We call this method Crop-Segmentation-Combination Framework (CSCF). We conducted experiments on the ICDAR 2013 and TextSeg datasets. The CSCF, embedded in Unet within the segment segmentation module, enhanced the text segmentation IoU by 5.80% on the ICDAR 2013 test dataset. Our suggested approach has been shown to notably enhance the efficiency of scene text segmentation. © 2023 ieee.

关键词： image segmentation Scene text segmentation Synthetic data synthetic image generations Text segmentation

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Building Annotated image Dataset for Myanmar Text to image Synthesis

Building Annotated Image Dataset for Myanmar Text to Image S...

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2023 ieee conference on computer applications, ICCA 2023

作者： Htwe, Nan Kham Pa, Win Pa University of Computer Studies Natural Language Processing Lab Yangon Yangon Myanmar

ISBN: (纸本)9781665435994

Text to image synthesis is the translation of images from the input language text. The learning process can become easier when the spoken words can visualize with the images. It is one of the popular research field in combination of NLP and computer vision. Generative Adversarial Networks (GAN) have growth in the generation of images from text descriptions. We build the baseline system of Myanmar text to image synthesis and a type of annotated images dataset because there is not efficient annotated image dataset to be used in this implementation. It was created by using partial part of Oxford-102 flowers dataset. Word2Vec algorithms is used to convert word to vectors for the input sentence to GAN. GAN is applied for generation of images from Myanmar language text. This is the first text to image generation using GAN in Myanmar. The two-evaluation metrics are used to measure the quality of images. The quality of the generated images is evaluated using Inception score. The Frechet Inception Distance (FID) is used to measure the distance between the real images (images from original dataset) and the generated images from the model. © 2023 ieee.

关键词： Generative adversarial networks

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Single-Photon Cameras image Reconstruction Using Vision Transformer 3

Single-Photon Cameras Image Reconstruction Using Vision Tran...

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3rd ieee international conference on computer Communication and Artificial Intelligence, CCAI 2023

作者： Wang, Xingzheng Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350335262

Single-photon camera is a novel camera type that utilizes image sensor with photon-counting capability. Recently, the potential of such sensors to achieve high spatial resolutions (e.g., 10/chip) and frame rates (e.g., 10/sec) is well-established. However, there is a significant difference in the output data between single-photon cameras and traditional CMOS sensor cameras. Therefore, conventional image reconstruction algorithms cannot be utilized. Vison Transformer has impressive performance on image processing tasks, and this paper will design a plug-and-play algorithm for reconstructing images from single-photon cameras and integrate image processing algorithms based on deep neural networks like ViT. The results demonstrate our methods can achieve improvement both in PSNR and SSIM. © 2023 ieee.

关键词： Cameras

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A Model for Content-Based image Retrieval Using Machine Learning 1

A Model for Content-Based Image Retrieval Using Machine Lear...

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1st international conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Mahajan, Yash Batta, Priya Sharma, Muskan Saxena, Devansh Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350315769

With the value of digital data increasing in recent years, substantial improvements have been made in image processing. It is now simpler to produce large image datasets. picture processing is carrying out a variety of processes to improve a picture or extract informative data from it. With several theories and models relating to the retrieval of images from the collection of images called datasets, it has developed into a crucial study subject within the disciplines of engineering and computer science. A well-liked paradigm for the retrieval of images, image retrieval based on content is quicker and more advantageous than textual indexation. The authors of this study provide a paradigm for feature-based picture retrieval. Based on a user query, the model searches a database for the most relevant photographs and presents them with the least amount of vector distance. The suggested model can greatly enhance image retrieval systems and the applications of such systems in numerous sectors. Overall, this study makes a positive contribution to attempts to advance image processing technologies and their usefulness in the contemporary world. © 2023 ieee.

关键词： Machine learning

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TRANSFORMATION CONSISTENCY FOR REMOTE SENSING image SUPER-RESOLUTION 30

TRANSFORMATION CONSISTENCY FOR REMOTE SENSING IMAGE SUPER-RE...

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30th ieee international conference on image processing (ICIP)

作者： Deng, Kai Yao, Ping Cheng, Siyuan Bi, Junyu Zhang, Kun Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China INRIA Saclay Ile De France Palaiseau France Inst Polytech Paris Paris France

ISBN: (纸本)9781728198354

Single image Super-Resolution (SISR) based on deep learning methods has been widely studied for applications on remote sensing images. With limited remote sensing images, most of the existing SISR methods simply adopt the regular data augmentation approaches (such as flip) in natural images to improve model performance. Considering the fact that remote sensing images are all taken from a bird's-eye view and objects appear in multiple directions, we first introduce rotation augmentation method in remote sensing images to promote diversity of samples dramatically, as rotation does not cause semantic problems like people standing upside down in natural images. However, image rotation at various angles implemented by interpolation will cause the inconsistent pixel distribution problem for the pixel level task. Thus, we propose Transformation Consistency Loss Function (TCLF) to narrow the gap between the augmented and original distribution, while expanding the feature space with rotation augmentation method. Extensive experiments are performed on UC-Merced Land-use dataset of 21 remote sensing scenes, and the results as well as ablation studies demonstrate our proposed method outperforms mainstream methods.

关键词： image processing Super-resolution Transformation consistency

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