Reversible data hiding algorithms based on prediction error histogram of rhombus prediction need excellent prediction performance to achieve more embedding capacity. However, the cost of improving the prediction accur...
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The current route recommendation algorithm has problems such as single data. This paper designs a route recommendation algorithm based on multidimensional data fusion, uses convolutional neural network (CNN) to extrac...
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With the extensive use of surveillance-based systems in the present age, it is recommended to employ lightweight deep neural networks (DNNs) that not only have small silicon footprints and low latency but also provide...
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The quality of image signals directly affects the performance of intelligent communication systems. This paper proposes a set of image enhancement and denoising algorithms to address image quality degradation in intel...
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ISBN:
(数字)9798331542696
ISBN:
(纸本)9798331542702
The quality of image signals directly affects the performance of intelligent communication systems. This paper proposes a set of image enhancement and denoising algorithms to address image quality degradation in intelligent communication systems. For image enhancement, we designed an adaptive histogram equalization algorithm based on blocks and a contrast adaptive optimization method, and implemented a detail enhancement algorithm combining multi-scale edge detection and enhancement. In terms of image denoising, we proposed adaptive median filtering, improved soft-threshold wavelet domain denoising, and non-local means algorithms to effectively suppress various types of noise. The system is developed using a hybrid $\mathrm{C}++$ and Python framework with parallel processing achieved through multithreading technology. Experimental results show that the proposed algorithms significantly improve image quality, processing efficiency, and system stability, providing reliable image signal processing support for intelligent communication systems.
images captured by mobile camera systems are subject to distortions that can be irreversible. Sources of these distortions vary and can be attributed to sensor imperfections, lens defects, or shutter inefficiency. One...
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ISBN:
(纸本)9781510666184;9781510666191
images captured by mobile camera systems are subject to distortions that can be irreversible. Sources of these distortions vary and can be attributed to sensor imperfections, lens defects, or shutter inefficiency. One form of image distortion is associated with high Parasitic-Light-Sensitivity (PLS) in CMOS image Sensors when combined with Global Shutters (GS-CIS) in a moving camera system. The resulting distortion appears as widespread semi-transparent purple artifacts, or a complex purple fringe, covering a large area in the scene around high-intensity regions. Most of the earlier approaches addressing the purple fringing problems have been directed towards the simplest forms of this distortion and rely on heuristic imageprocessingalgorithms. Recently, machine learning methods have shown remarkable success in many image restoration and object detection problems. Nevertheless, they have not been applied for the complex purple fringing detection or correction. In this paper, we present our exploration and deployment of deep learning algorithms in a pipeline for the detection and correction of the purple fringing induced by high-PLS GS-CIS sensors. Experiments show that the proposed methods outperform state-of-the-art approaches for both problems of detection and color restoration. We achieve a final MS-SSIM of 0.966 on synthetic data, and a distortion classification accuracy of 96.97%. We further discuss the limitations and possible improvements over the proposed methods.
imageprocessing in the scrambled area has received increasing attention for its many anticipated uses, such as providing efficient and safe solutions for protection-saving applications in untrustworthy environments. ...
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Multimodal remote sensing images describe the same surface scene from different viewpoints, and can gain more reliable information about the earth's surface. As a result, fusion classification of multi-modal remot...
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The proceedings contain 149 papers. The topics discussed include: effects of AI on smart agriculture: a case study of digital agriculture base;image retrieval-based product identification for automatic checkout system...
ISBN:
(纸本)9781643684444
The proceedings contain 149 papers. The topics discussed include: effects of AI on smart agriculture: a case study of digital agriculture base;image retrieval-based product identification for automatic checkout systems;application of lightweight emotion recognition model in intelligent construction site monitoring;research on the application of smart wearable in the emotional management of the elderly;artificial intelligence technology in the field of broadcasting and hosting;intelligent inspection method for photovoltaic modules based on imageprocessing and deep learning;application of AI algorithms in power system load forecasting under the new situation;research on the intelligent operation and maintenance control system of power distribution network based on big data technology;research progress of intelligent rail transit train control system;and design and implementation of intelligent potted plant management system based on Internet of Things.
This method includes two steps: (1) The first step is to use neural network to extract depth from the collected hyperspectral images, and then use it to classify the hyperspectral images. (2) The second step is to use...
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Data fusion, which involves integrating data from multiple sources, is increasingly valuable across various fields due to its ability to enhance information quality, accuracy, and reliability. This process enables a m...
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ISBN:
(纸本)9781510673816;9781510673809
Data fusion, which involves integrating data from multiple sources, is increasingly valuable across various fields due to its ability to enhance information quality, accuracy, and reliability. This process enables a more comprehensive understanding of complex phenomena by merging diverse datasets, providing insights that are otherwise unattainable. In the realm of remote sensing, where precise data acquisition is critical, fusion techniques have become indispensable, benefiting applications such as object detection, classification, and change detection. While much emphasis has been placed on spatial sharpening techniques in published studies, there remains a notable gap in establishing robust workflows for both lab-based and UAS-based remote sensing data fusion, particularly in the near-infrared (VNIR) and short-wave infrared (SWIR) regions. This study aims to investigate VNIR-SWIR fusion using data sourced from a medieval manuscript in a controlled laboratory environment and from UAS-based sensors in a real-world setting, addressing differences in system parameters and processing workflows. Despite challenges such as image registration issues, our analysis has yielded promising results, underscoring the importance of ongoing refinement in fusion methodologies to ensure comprehensive data interpretation and analysis across diverse datasets and environments.
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