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Research on image optimization of graphic design based on 3D laser vision technology 2

Research on image optimization of graphic design based on 3D...

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2nd International conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Yang, Dianqing Liu, Renjing Visual Design College Keimyung University Dalseo-gu Daegu704-701 Korea Republic of

ISBN: (纸本)9781510689053

To prevent image distortion, this paper explores methods for enhancing and optimizing graphic design images using 3D laser vision technology. The process involves collecting graphic design image data, mapping 3D laser information onto the surface of the image to form an information map, and utilizing this data for locally weighted complexity processing. A histogram of the graphic design image is constructed, followed by scanning and grayscale transformation to eliminate noise. The image histogram is then segmented using a block-based local enhancement method to achieve the optimization and enhancement of the graphic design image. Experimental results show that under different noise standard deviations, the average peak signal-to-noise ratio (PSNR) of the proposed method is 36.62 dB, and the average structural similarity index (SSIM) is 0.9526. This method effectively reduces noise and enhances the visual quality of graphic design images, improving the ability of the human eye to discriminate information in these images. © 2025 SPIE.

关键词： image enhancement

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Global contextual enhanced attention-guided domain adaptation for dual-energy x-ray image segmentation of ulna and radius

Global contextual enhanced attention-guided domain adaptatio...

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2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Yang, Yongxin Wang, Wei Jiang, xiaoming Chongqing Engineering Research Center of Medical Electronics and Information Technology Chongqing University of Post and Telecommunications Chongqing400065 China

ISBN: (纸本)9781510680425

Dual-energy x-ray absorptiometry (DxA) is the primary modality for bone mineral density (BMD), offering a reliable clinical indicator for orthopedic diagnosis and *** practical applications, there are several factors that could lead to variations in the grayscale distribution of dual-energy x-ray *** domain shift of the dual-energy x-ray images often makes neural networks used for ulna and radius segmentation ineffective, significantly impacting bone density *** this paper, we propose an unsupervised Global Contextual Enhanced Attention-guided Domain Adaptation (GCEADA) framework to enhance the performance of the network in the ulna and radius segmentation task of the target *** proposed method extracts global context information to enhance attention expression, obtaining more representative domain invariant *** evaluate the GCEADA in two cross-domain tasks and conduct ablation experiments to assess the performance of each *** results indicate that the proposed attention module effectively improves the feature extraction capability of the discriminator and the discriminability and transferability of the framework. © 2024 SPIE.

关键词： image segmentation

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Radar based Continuous Indoor Activity Recognition using Deep Learning 6

Radar based Continuous Indoor Activity Recognition using Dee...

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conference on Big Data VI - Learning, Analytics, and applications

作者： Breaker, Henry Hamza, Syed Ali Widener Univ Sch Engn Chester PA 19013 USA

ISBN: (纸本)9781510673915;9781510673908

Radar-based sensing emerges as a promising alternative to cameras and wearable devices for indoor human activity recognition. Unlike wearables, radar sensors offer non-contact and unobtrusive monitoring, while being insensitive to lighting conditions and preserving privacy as compared to cameras. This paper addresses the task of continuous and sequential classification of daily life activities, unlike the problem to isolate distinct motions in isolation. Upon acquiring raw radar data containing sequences of motions, an event detection algorithm, the Short-Time-Average/Long-Time-Average (STA/LTA) algorithm, is utilized to detect individual motion segments. By recognizing breaks between transitions from one motion type to another, the STA/LTA detector isolates individual activity segments. To ensure consistent input shapes for activities of varying durations, image resizing and cropping techniques are employed. Furthermore, data augmentation techniques are applied to modify micro-Doppler signatures, enhancing the classification system's robustness and providing additional data for training.

关键词： image segmentation Radar Radar signal processing Education and training Matrices Doppler effect Sensors Motion detection image classification image processing algorithms and systems

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Digital signal processing techniques for image enhancement and restoration 3

Digital signal processing techniques for image enhancement a...

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3rd International conference on Advanced Algorithms and signal image processing, AASIP 2023

作者： Qu, Chenyao Tangge, Yanhao Yang, Guang Chumen High School Taizhou317605 China Nanjing Jinling High School Nanjing210002 China School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510668522

The image processing technology is widely used in digital signal processing, biomedical engineering, and industrial automation control. In image processing, image enhancement and restoration techniques are used in many imaging applications, such as astrophotography, medical imaging camera, film production, and infrared imaging. At the same time, image enhancement and restoration technology based on both deep learning and neural network is a major research hotspot in the area of image processing. This paper reviews the related research of image enhancement and restoration technology. In terms of image enhancement, the working principle of its technology based on spatial domain and frequency domain and contrast enhancement technology, including color image contrast enhancement, are introduced. In image restoration, median filtering technology and the Lucy-Richardson (LR) algorithm are introduced, including the adaptive median filtering algorithm and the improved LR algorithm based on the Gaussian low-pass filter. Finally, the merits and demerits of these technologies are discussed, and the development prospect of image enhancement and restoration technology has prospected. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image reconstruction

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Powerful Lossy Compression for Noisy images

Powerful Lossy Compression for Noisy Images

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IEEE International conference on Multimedia and Expo (ICME)

作者： Cai, Shilv Liang, xiaoguo Cao, Shuning Yan, Luxin Zhong, Sheng Chen, Liqun Zu, xu Huazhong Univ Sci & Technol Wuhan Peoples R China Natl Key Lab Multispectral Informat Intelligent P Beijing Peoples R China

ISBN: (纸本)9798350390155;9798350390162

image compression and denoising represent fundamental challenges in image processing with many real-world applications. To address practical demands, current solutions can be categorized into two main strategies: 1) sequential method;and 2) joint method. However, sequential methods have the disadvantage of error accumulation as there is information loss between multiple individual models. Recently, the academic community began to make some attempts to tackle this problem through end-to-end joint methods. Most of them ignore that different regions of noisy images have different characteristics. To solve these problems, in this paper, our proposed signal-to-noise ratio (SNR) aware joint solution exploits local and non-local features for image compression and denoising simultaneously. We design an end-to-end trainable network, which includes the main encoder branch, the guidance branch, and the signal-to-noise ratio (SNR) aware branch. We conducted extensive experiments on both synthetic and real-world datasets, demonstrating that our joint solution outperforms existing state-of-the-art methods.

关键词： Joint Solution image Compression image Denoising Neural Networks

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Comparative Analysis of image Prediction Using Convolutional Neural Network Models

Comparative Analysis of Image Prediction Using Convolutional...

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2024 IEEE International conference on Intelligent signal processing and Effective Communication Technologies, INSPECT 2024

作者： Choutapally, Akshara Ansari, Irshad Ahmad ABV-Indian Institute of Information Technology and Management Department of Information Technology Gwalior India ABV-Indian Institute of Information Technology and Management Department of Electrical and Electronics Engineering Gwalior India

ISBN: (纸本)9798350379525

This study provides an analysis of image prediction using convolutional neural networks (CNNs) with varying kernel sizes using different image resolutions. The study inquires into four distinct models. Each of them with different combinations of convolutional kernels: Model-1 (3x3), Model-2 (3x3, 5x5), Model-3 (3x3, 5x5, 7x7), and Model-4 (3x3, 5x5, 7x7, 9x9). The models were assessed on images of three different sizes: 128x128, 64x64, and 32x32 pixels, and using the performance metric Peak signal-to-Noise Ratio (PSNR),the predicted results are compared. It is uncovered that more the diverse the set of kernel sizes are, the better the model performs, irrespective of the image resolutions. The better the image resolution, the more information can be extracted from the images, which in turn gives better prediction accuracy. The highest PSNR values achieved are by Model-4 for all image resolutions, with 33.87 dB for 128x128, 28.12 dB for 64x64, and 25.86 dB for 32x32 images. This indicates that using a combination of kernels enables the model to capture both local and global image features more effectively, leading to improved prediction accuracy. In contrast, models with fewer or single kernel sizes exhibited significantly lower performance, particularly as the image size decreased. These results emphasize on the importance of multi-scale feature extraction in CNNs for image prediction tasks, suggesting that models with diverse kernel sizes are more robust and adaptable to varying image resolutions, how the image resolutions must be carefully selected for the relevant task. These insights have a great impact in practical applications like Reversible Data Hiding where image prediction is the major portion of the task and it's important to understand the appropriate kernel sizes, image resolutions, and appropriate number of images for the optimal prediction accuracy. © 2024 IEEE.

关键词： Prediction models

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An image encryption algorithm based on non-degenerate chaos and dynamic Bi-directional permutation 16

An image encryption algorithm based on non-degenerate chaos ...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Liao, Yunlong xu, Qingyang Zhang, Qiyuan xing, Zheng Yuan, xiaochen Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781510689251

In the wake of the accelerated advancement of Internet of Things(IoT) technology, a significant volume of multimedia information is emerging as the dominant component of IoT applications. However, this information also led to big security risks. Therefore, a secure and reliable image encryption scheme based on non-degenerate hyper chaos and dynamic bidirectional permutation is proposed for the characteristic of digital images which the primary information carrier in the IoT communication environment. The scheme uses non-degenerate discrete chaos to generate pseudo-random sequences and applies them to the encryption process involving image confusion, dynamic permutation, and dynamic diffusion. Through theoretical analysis and experimental verification, the algorithm exhibits high sensitivity to both the plaintext images and the ciphertext images. Concurrently, it can effectively resist common noise attacks and cryptographic attacks. In conclusion, the proposed scheme offers superior encryption capabilities and stands as a secure solution, compatible with a diverse range of IoT environments. © 2025 SPIE.

关键词： Chaos theory

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RDM: A Light Field Super-Resolution Model Improved with Retinex Decomposition 16

RDM: A Light Field Super-Resolution Model Improved with Reti...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Lyu, Wenqi Ke, Wei Sheng, Hao Ma, xiao Liu, Lixue Faculty of Applied Sciences Macao Polytechnic University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781510689251

Light field cameras hold significant value in applications such as depth estimation, 3D video acquisition, and image super-resolution. Compared to traditional single-image super-resolution methods, light field images can capture object information from multiple spatial and angular perspectives, which is more conducive to capturing detail information. However, current advanced light field super-resolution models still exhibit issues like blurriness, jaggedness, and dimness in 4x light field super-resolution. To address this, we propose a novel light field super-resolution framework that utilizes the Retinex decomposition concept to solve common problems such as ghosting and overlap, thereby improving image quality. By decomposing the light field image into illumination and reflection components and processing them separately, our model significantly enhances detail preservation and color consistency. Experimental results on multiple datasets demonstrate that our model exhibits outstanding performance, both subjectively and objectively, highlighting the effectiveness of this framework and its broad application potential in the field of computational imaging. © 2025 SPIE.

关键词： image acquisition

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A personal retrospective on the development of the DLP and related spinoffs 16

A personal retrospective on the development of the DLP and r...

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conference on Emerging Digital Micromirror Device Based Systems and applications xVI

作者： Cohn, Robert W. Univ Louisville Elect Res Inst Louisville KY 40209 USA Univ Louisville Nanotechnol Ctr Louisville KY 40209 USA

ISBN: (纸本)9781510670617;9781510670600

In 1985 I joined Texas Instruments' (TI's) Deformable Mirror Device ( DMD) group to develop applications of the cantilever device in coherent optical signal processing. At that time I witnessed the "aha discovery" that led to the invention of the DLP. It is interesting to consider the many years of effort that led Larry Hornbeck to this commercially successful implementation, not just the technology, but the efforts to sustain the project through sponsored R&D. While TI viewed the only sustainable market as (incoherent) display applications, the DMD group sustained the effort with DoD funding for coherent and incoherent optical signal processing systems, including matched filter correlators, digital optical switches, optical crossbar switches and related neural network processors. For coherent signal processing the need for a 2p phase-only (piston-motion pixel) spatial light modulator (SLM) was readily apparent to the sponsors. While TI saw little commercial justification for the phase-only device, this need inspired me around 1991 to develop a new class of real-time computer-generated holography algorithms referred to a pseudorandom encoding, in which each phase-only pixel is encoded with a desired magnitude and phase. The optical Fourier transforms of the modulation enabled my developments of multi-spot object targeting and laser tweezer systems. Around 2005 I began using Digital Light processing (DLP) developer kits in place of scanners to time-share images with a small number of detectors. One system using a single, high sensitivity detector together with well- chosen DLP frames quickly forms a "partial image" of a point-like scene objects - arguably, an early version of compressive sensing. This paper concludes with recommendations on optimizing the performance and applications of, and potential markets for TI's recently demonstrated phase-only DLP.

关键词： Deformable mirror device digital light processing optical information processing compressive sensing

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Research on human posture recognition based on channel state information in wireless signal 4

Research on human posture recognition based on channel state...

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4th International conference on signal image processing and Communication, ICSIPC 2024

作者： Li, Yuangang Dept. Northeastern University Shenyang China

ISBN: (纸本)9781510682467

In the current research landscape, human pose recognition technology is increasingly gaining attention, particularly in the areas of interactive design, health monitoring, and security applications. However, traditional computer vision-based methods for human pose recognition rely heavily on lighting conditions and face limitations when dealing with complex environments. To overcome these constraints, this paper explores a method of human pose recognition based on wireless signal's Channel State Information (CSI). This method, unlike traditional computer vision technologies, does not depend on lighting conditions, making it more suitable for complex environments and offering better protection of user privacy. Traditional CSI-based methods for human pose recognition mainly employ Long Short-Term Memory (LSTM) neural networks, but these methods are limited in computational efficiency due to constraints in parallelization capabilities. To address this, the paper introduces a novel network architecture named "CSI Transformer," which combines Temporal Convolutional Networks (TCN) and the Transformer architecture for efficient processing of CSI data. Initially, the data undergoes preliminary feature extraction using temporal convolutional networks, followed by deep feature analysis and pose prediction through the Vision Transformer architecture. Experimental results indicate that, compared to traditional LSTM-based methods, the CSI Transformer can recognize human poses more efficiently while ensuring accuracy and safeguarding data privacy. This study offers a new perspective and method for the application of wireless signals in human pose recognition, highlighting its significance in advancing related technologies and applications. Moreover, the introduction of the CSI Transformer provides an effective solution to issues such as low computational efficiency and privacy protection, suggesting a broader application prospect for wireless signal-based human pose recognition t

关键词： Differential privacy

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