Numerical simulation and characterization of speckle noise for laser Doppler vibrometer on moving platforms (LDVom)

Yang, Jianzhong; Dollevoet, Rolf; Li, Zili

doi:10.1016/j.optlaseng.2022.107135

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…Variations in brightness [29], contrast [30], gamma correction [31], or the addition of random noise, such as Gaussian or speckle noise [32], to simulate sensor noise or specific lighting conditions are all examples of light noise. These enhancements can be used individually or in tandem to create more complex and diverse lighting conditions.…”

Section: B Light Signal Enhancementmentioning

confidence: 99%

From Augmentation to Inpainting: Improving Visual SLAM With Signal Enhancement Techniques and GAN-Based Image Inpainting

Theodorou,

Velisavljevic,

Dyo

et al. 2024

IEEE Access

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This paper undertakes a comprehensive investigation that surpasses the conventional examination of signal enhancement techniques and their effects on visual Simultaneous Localization and Mapping (vSLAM) performance across diverse scenarios. Going beyond the conventional scope, the study extends its focus towards the seamless integration of signal enhancement techniques, aiming to achieve a substantial enhancement in the overall vSLAM performance. The research not only delves into the assessment of existing methods but also actively contributes to the field by proposing innovative denoising techniques that can play a pivotal role in refining the accuracy and reliability of vSLAM systems. This multifaceted approach encompasses a thorough exploration of the intricate relationships between signal enhancement, denoising strategies, their cumulative impact on the performance of vSLAM in real-world applications and the innovative use of Generative Adversarial Networks (GANs) for image inpainting. The GANs effectively fill in missing spaces following object detection and removal, presenting a novel state-of-theart approach that significantly enhances overall accuracy and execution speed of vSLAM. This paper aims to contribute to the advancement of vSLAM algorithms in real-world scenarios, demonstrating improved accuracy, robustness, and computational efficiency through the amalgamation of signal enhancement and advanced denoising techniques.INDEX TERMS signal enhancement, denoising techniques, visual SLAM, object detection, simultaneous localization and mapping(SLAM), object detection and generative adversarial network (GAN).

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Section: B Light Signal Enhancementmentioning

confidence: 99%