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Research on the Application of Multi-Channel Display Technology in Virtual Scene image Generation 5

Research on the Application of Multi-Channel Display Technol...

引用

5th IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Liu, Yuanlin Wang, Lingling Haikou University of Economics Haikou571127 China

ISBN: (纸本)9798331533694

This study proposes an innovative algorithm based on DCNN and multi-channel image fusion, aiming to improve the quality and efficiency of virtual scene image generation. The algorithm extracts depth information and texture features in virtual scenes through DCNN, and combines multi-channel image fusion technology to optimize and fuse the information of different image channels (such as color, depth, normal, etc.), thereby significantly improving the image rendering effect. Through adaptive weight adjustment and image fusion strategy, the image detail performance is optimized, especially in scenes with complex lighting and rich textures, the rendering quality is significantly improved. Experimental results show that the image generation system based on this algorithm improves image quality by 15%, rendering speed by 25%, and computing resource consumption by 20%. When processing highly complex virtual scenes, the system can achieve real-time rendering and meet the performance requirements of VR and AR applications. This study provides a new and efficient solution for image generation in virtual reality and augmented reality, and demonstrates the great potential of combining multi-channel display technology with deep learning in improving rendering effects. © 2025 IEEE.

关键词： Virtual environments

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Comparative analysis of optical system configurations for enhanced retinal imaging in smartphone-based fundus cameras

Comparative analysis of optical system configurations for en...

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2024 International conference on Interdisciplinary Physics, ICIPs 2024

作者： Rosyadah, Aprina Nasution, Aulia M.T. Surabaya Indonesia

This paper presents a comparative study on the optical system design for a smartphone-based fundus camera to enhance retinal imaging quality. Three optical system configurations were evaluated using ANSYS Zemax OpticStudio 2024 R1 software: Design I (20D aspheric lens), Design II (40D aspheric lens), and Design III (60D aspheric lens). The optical systems were evaluated by analysing: (i) the illumination path from the light source to the retina, and (ii) the imaging path from the retina to the smartphone camera's sensor. The results were evaluated using image metric quantities, including the spot diagram, transverse ray fan plot, and TTF MTF vs Field, to represent the image quality of each optical system. Additionally, the illumination and imaging paths were analysed to assess the overall image quality. The findings indicated that Design III, with the 60D aspheric lens, demonstrated superior image quality in terms of spot diagram, ray fan plot, and MTF performance, making it the most effective design. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Ophthalmology

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FPSNet: Focus-Perceptual-Semantic Full Flow Visual Redundancy Predicting for Camera image 7th

FPSNet: Focus-Perceptual-Semantic Full Flow Visual Redundanc...

引用

7th Chinese conference on Pattern Recognition and Computer Vision

作者： Xiao, Xiongwei Hubei Wuhan China

ISBN: (纸本)9789819786916;9789819786923

With the rapid popularization of electronic devices, the large amount of data generated by camera imaging poses a huge challenge to the limited storage capacity and communication bandwidth. Achieving higher compression ratios without sacrificing visual quality remains a fundamental challenge for image compression. In this paper, we propose a novel full flow bidirectional visual threshold estimation method for camera imaging perceptual compression. Specifically, we study the features from camera imaging to visual perception to semantic understanding, and characterize them with focus identification, perceptual distribution, and semantic segmentation respectively. We also carefully design feature extraction networks suitable for each feature type. In addition, we draw inspiration from the bidirectional perceptual mechanism of the human visual system and propose a feature extraction framework that adopts top-down and bottom-up methods. We further enhance our model by regulating and fusing bidirectional perceptual features through a gated decoding structure. Extensive experimental validation on benchmark datasets confirms that our FPSNet significantly improves the accuracy of visual redundancy prediction.

关键词： Camera image Visual redundancy image compression

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Diffusion Model Based image Reconstruction in Lensless Imaging

Diffusion Model Based Image Reconstruction in Lensless Imagi...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Verma, Ashish Boominathan, Vivek Veeraraghavan, Ashok Seelamantula, Chandra Sekhar Department of Electrical Engineering Indian Institute of Science Bengaluru560 012 India Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

ISBN: (纸本)9798350368741

Lensless imaging systems eliminate the need for lenses by employing an encoding element to multiplex incident light signals, which are then captured directly onto a bare camera sensor. They present a promising alternative to traditional lens-based imaging systems by offering significant advantages in terms of compactness, versatility, and cost. Due to the multiplexed nature of measurements, image reconstruction takes place computationally. However, existing techniques for image reconstruction in lensless imaging fall short of the image quality offered by traditional lens-based imaging. In this work, we consider the application of diffusion models, a class of deep generative models, for image reconstruction in a lensless imaging modality. These models currently achieve state-of-the-art performance in image generation. Specifically, we focus on the PhlatCam lensless system, which consists of a coded phase mask as the encoding element placed close to the camera sensor. We use a ControlNet based diffusion model to improve the perceptual quality of image reconstruction. The performance is measured in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The proposed method improves the performance in these metrics for synthetic measurements. For real measurements, the improvement in image quality comes at the expense of a small bias in color, which is attributed to the generative nature of the diffusion prior itself. © 2025 IEEE.

关键词： Computational imaging diffusion model generative model lensless imaging stable diffusion

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Research on Digital Creative Content Generation system Based on Artificial Intelligence

Research on Digital Creative Content Generation System Based...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Lv, Guanfeng Guangzhou Huashang Vocational College Guangdong Guangzhou511300 China

ISBN: (纸本)9798331504205

A new algorithm framework is proposed, which combines the self-attention mechanism and the transformer model, to enhance the efficiency and quality of the creation of digital creative content. The system realizes the semantic understanding and generation tasks of multimodal data by designing a multi-layer Transformer architecture, and fully exploits the context modeling ability of the self-attention mechanism. To verify the performance of the algorithm, this paper simulates the model in image generation, text generation and multimodal content generation tasks. The test results show that the system has achieved significant improvements in the coherence, diversity and innovation of the generated content. Experiments on a public dataset show that the BLEU score of the generated text content is 12.3% higher than that of the traditional method, the FID index of image generation is reduced by 15.7%, and the semantic consistency score of multimodal generation reaches 92.4%. These results prove the superiority and practicality of the proposed algorithm. This research not only provides a new way to generate digital creative content, but also provides a theoretical basis for the further integration of AI and creative industry. © 2025 IEEE.

关键词： algorithm combining self-attention mechanism with Transformer model Artificial intelligence digital creative content generation system

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Design of Rotary Cryocoolers Drive Control system Based on FPGA 4

Design of Rotary Cryocoolers Drive Control System Based on F...

引用

4th International Computational Imaging conference, CITA 2024

作者： Wang, Fei Su, Junbo Wang, Haihu Dong, Wei Hu, Zhibin Kunming Institute of Physics Kunming China

ISBN: (纸本)9781510688834

Cooled infrared detectors are the core components of high-performance infrared systems, utilizing Stirling cryocoolers to maintain a stable operating temperature. This stabilizes detector temperature, suppresses dark current, mitigates noise, enhances sensitivity, and improves image quality. Traditional infrared systems operate with separate image processing and cryocooler drive components, typically managed by an FPGA and a microcontroller or DSP, respectively, increasing development costs and complexity. This study proposes integrating the cryocooler control electronics (CCE) of rotary cryocoolers into the FPGA-based infrared image processing mainboard, leveraging the resources of the image processing FPGA to simplify system architecture, reduce PCB area, component count, communication delays, and failure points, thereby enhancing system reliability and response speed. In hardware design, principles of miniaturization, low power consumption, and autonomous control are followed, and a fully localized CCE solution using domestic components is developed. The hardware design prioritizes space and power reduction while ensuring robust performance. In software, a BLDC motor drive method based on FPGA is developed, using a temperature closed-loop digital PID algorithm to adjust the PWM signal duty cycle, achieving rapid and precise temperature control. Experimental results demonstrate that the system meets the expected temperature control accuracy, with fast tracking speed and good stability. Integrating the cooler drive control into FPGA simplifies the overall system design while enhancing reliability and responsiveness. This approach meets the infrared systems' requirements for miniaturization, low power consumption (SWaP), and low cost, offering a universal methodology for rotary cryocooler drive control, demonstrating notable application value. © 2025 SPIE.

关键词： Infrared detectors

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Secure and High-Fidelity Medical image Generation: A GAN-based Approach for Steganography 3

Secure and High-Fidelity Medical Image Generation: A GAN-bas...

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sumitra, B. Selvin Ebenezer, S. Vallathan, G. Sundaramurthy, B. Dorsela, Venkata Rami Reddy Department of ECE Geethanjali College of Engineering & Technology Hyderabad India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Geethanjali College of Engineering and Technology Hyderabad India Salem India

ISBN: (纸本)9798331527549

Traditional image steganography methods embed secret data into cover images but often face detection by steganalysis tools due to detectable pixel modifications, which increase the risk of data leakage. These techniques also typically degrade image quality and offer limited capacity. To overcome these challenges, a novel approach for medical imaging is proposed, integrating Generative Adversarial Networks (GANs), PatchGAN, Adaptive LSB steganography, and a dual attention mechanism. PatchGAN improves fine-grained texture details, while Adaptive LSB securely embeds sensitive data within the images. The dual attention mechanism highlights critical regions, enhancing both image clarity and diagnostic accuracy. This method improves image quality and security, benefiting clinical decision-making and patient outcomes. The system is evaluated against traditional methods like GAN and IDGAN using metrics such as embedding capacity, classification accuracy (stego, cover, global), and discriminator loss. Results demonstrate that the proposed system outperforms existing techniques, offering significant improvements in both performance and security for medical imaging applications. © 2025 IEEE.

关键词： Generative adversarial networks

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A Hybrid GAN-CNN Framework for Low-Light image Enhancement Combining Structural Noise Reduction and Perceptual quality

A Hybrid GAN-CNN Framework for Low-Light Image Enhancement C...

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2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Gobinath, R. Boopathi, R. Vijayalakshmi, S. Disney, D. Anu Priyadharshini, P. Nirmala Maheshwari, K. E.G.S. Pillay Engineering College Nagapattinam611002 India Karpaga Vinayaga College of Engineering and Technology Department of Electrical and Electronic Engineering Tamilnadu 603308 India Sona College of Technology Department of Electronics and communication Engineering Tamilnadu Salem636005 India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India Adithya Institute of Technology Department of Information Technology Coimbatore641107 India Sir Issac Newton College of Engineering and Technology Department of Artificial Intelligence and Data Science Nagapattinam611 102 India

ISBN: (纸本)9798331509828

Low light region is one of the difficult scenarios for surveillance system, especially noisy, low-quality images may lead to poor object detection and scene analysis. To tackle this problem, we introduce a hybrid GAN - CNN framework for low light image denoising and quality enhancement. We develop a framework that combines the strengths of Generative Adversarial Networks (GANs) for photorealistic image generation and Convolutional Neural Networks (CNNs) for robust noise reduction. First denoising and feature extraction is done by the CNN module, and the GAN part further refines the denoised output to improve the perceptual quality of the generated images. We train and validate the proposed model in standard low light image dataset and show superior PSNR, SSIM, and visual clarity compared to state of art methods. We show that the hybrid GAN-CNN model successfully trades between image denoising accuracy and visual realism, thereby providing a viable alternative for enhancing surveillance performance in lowlight conditions. Keywords: image denoising, GAN-CNN hybrid, low-light enhancement, PSNR, SSIM, surveillance systems. © 2025 IEEE.

关键词： Gallium nitride

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Adaptive Dual Attention Fusion Network for RGB-D Surface Defect Detection 7th

Adaptive Dual Attention Fusion Network for RGB-D Surface Def...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Jiang, Xiaoheng Liu, Jingqi Yan, Feng Lu, Yang Jin, Shaohui Liu, Hao Xu, Mingliang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Minist Educ Engn Res Ctr Intelligent Swarm Syst Zhengzhou 450000 Peoples R China Natl Supercomp Ctr Zhengzhou Zhengzhou 450000 Peoples R China

ISBN: (纸本)9789819786916;9789819786923

Defect detection plays an important role in the industrial production and manufacturing sectors, directly impacting product quality and production costs. Most defect detection methods relying on RGB images often encounter challenges such as similarity between foreground and background. RGB-D image that introduces the depth channel can combine RGB and depth information to improve the defect detection performance. However, most RGB-D defect detection methods usually suffer from lower-quality depth images and can not fully take advantage of the two modalities. To tackle this problem, we propose an Adaptive Dual Attention Fusion Network (ADAFNet). ADAFNet is a three-branch decoder-encoder structure that extracts RGB and depth features and synchronously fuses them via a dual-attention dual-modality fusion (DADMF) module. DADMF first carries out dual-modality fusion on the channel and spatial attention features, respectively. Then it makes dual-attention fusion on the RGB and depth features, respectively. Finally, DADMF combines the resulting RGB and depth features using an adaptive weighting factor. Experiments conducted on the NEU RSDDS-AUG, NJU2K, and NLPR datasets demonstrate that our method outperforms other state-of-the-art approaches.

关键词： Defect detection RGB-D image Dual-attention fusion Dual-modality fusion

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Real-Time Flexible Accelerator for Fisheye image Correction Based on Zynq SoC 11

Real-Time Flexible Accelerator for Fisheye Image Correction ...

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11th International conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Oh, HoBin Hwang, Gyu Hyeon Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798331509293

Fisheye cameras' broad field of view makes them ideal for a variety of applications, such as robotic navigation, drone vision, and car surround-view systems. Fisheye eyeglasses' intrinsic nonlinear distortion, however, makes it difficult to accurately interpret images and analyze them later. This research proposes a Zynq system on Chip (SoC)-based accelerator to dynamically restore fisheye lens image distortion in real time. The proposed system improves the limitations of conventional field programmable gate array (FPGA) designs that require fixed parameters and leverages the Python Productivity for Zynq (PYNQ) framework to enable dynamic parameter modification without reprogramming. Experimental results show that the proposed system processes images with a resolution of 960x640 at a speed of 135.7 frames per second (fps), achieving more than 20 times faster performance compared to software-based solutions. Additionally, it can maintain real-time performance even at Full HD resolution. We also evaluated the accuracy by comparing the fisheye distortion correction of four interpolation techniques provided by OpenCV with the quality indicators Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). It is also flexible enough to allow the system to dynamically adapt to environmental changes and process images efficiently. This research provides a highly flexible, real-time fisheye distortion correction system for various industries such as robotics, automotive, and drones. © 2025 IEEE.

关键词： Nonlinear distortion

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