System for optimized real-time image dehazing through hybrid convolutional neural network graph neural networks deep learning model, has neural network deep learning model for maximizing photo dehazing, where convolutional neural network extracts local characteristics from photos
	KUMAR D; SUNDERRAO M
	2025-04-12
专利权人	NAT TECHNOLOGY JAMSHEDPUR INST (NATE-Non-standard)
申请日期	2025-04-12
专利号	IN202531035742-A
成果简介	NOVELTY - The system has a convolutional neural network-graph neural network (CNN-GNN) deep learning model for maximizing photo dehazing, where the CNNs extract local characteristics from photos and GNN collects global dependencies and contextual information for boosting dehazing accuracy and efficiency. Accelerating deep learning using field programmable gate array (FPGA) hardware such as PYNQ-Z2 is used to boost performance. FPGAs can speed the hybrid CNN-GNN model and dehaze images in real time with minimal latency. The system meets application speed and accuracy requirements with deep learning and FPGA acceleration. USE - System for optimized real-time image dehazing through a hybrid convolutional neural network graph neural networks deep learning model on FPGA accelerated PYNQ-Z2 platform for real-time applications e.g. autonomous cars, surveillance i.e. aerial surveillance, and remote sensing applications (all claimed). Can also be used in environmental monitoring, and research purposes. ADVANTAGE - The system enhances real-time picture dehazing with a hybrid deep learning model that integrates CNNs and GNNs on an FPGA-accelerated PYNQ-Z2 platform. The system improves dehazing by maximizing local and global image qualities using a hybrid method.
IPC 分类号	G06N-003/045 ; G06N-003/08 ; G06T-001/20 ; G06T-005/73 ; G06V-010/44
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/13178
专题	中国科学院新疆生态与地理研究所
作者单位	NAT TECHNOLOGY JAMSHEDPUR INST (NATE-Non-standard)
推荐引用方式 GB/T 7714	KUMAR D,SUNDERRAO M. System for optimized real-time image dehazing through hybrid convolutional neural network graph neural networks deep learning model, has neural network deep learning model for maximizing photo dehazing, where convolutional neural network extracts local characteristics from photos. IN202531035742-A[P]. 2025.

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