Study on the Influence of Image Noise on Monocular Feature-Based Visual SLAM Based on FFDNet

Cao, Like; Ling, Jie; Xiao, Xiao-Hui

doi:10.3390/s20174922

Cited by 16 publications

(7 citation statements)

References 34 publications

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“…In visually degraded scenes such as turbid underwater [11] and dark scenes [12], to ensure that sufficient features are detected, image enhancement methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE) or Histogram Equalization (HE) are often used in visual SLAM methods for image preprocessing. To mitigate the effects of radiation noise, adding an image denoising method is a conventional and effective solution [13]. In the field of computer vision, many image denoising methods have been proposed.…”

Section: Image Preprocessingmentioning

confidence: 99%

Robust monocular visual inertial odometry in γ radioactive environments using edge-based point features

et al. 2023

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In the γ radioactive environment, high-energy photons induce degradation of the image sensor, which effects feature detection and tracking in the Visual Inertial Odometry (VIO) algorithm and deteriorates its localization performance. To address this issue, in this work, we propose a monocular VIO method using edge-based point features. To mitigate the effects of radiation noise, firstly, in the image preprocessing module, the median filter is used for real-time image denoising. Secondly, in the data association module, both Shi-Tomasi and edge-based point features are detected. The edge-based point feature is the endpoint or corner point in the salient edge map, which is more robust to radiation noise. Then, the bi-directional motion parallaxes and the RANdom SAmple Consensus (RANSAC) method are exploited to reject outliers. Finally, the point features measurements and Inertial Measurement Unit (IMU) pre-integration measurements are added into a tightly-coupled sliding window optimization VIO framework for localization estimation. The proposed method is verified by synthetic and real γ radioactive environment datasets. The experimental results show that the proposed method achieves more accurate and robust localization than the state-of-the-art VIO approaches in the γ radioactive environments.

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Section: Image Preprocessingmentioning

confidence: 99%

Robust monocular visual inertial odometry in γ radioactive environments using edge-based point features

et al. 2023

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“…Therefore, noise in the image and unnecessary blobs in the background should be removed. Various noise removal or denoising methods can be used for this purpose [52]. By removing the noises, only features are extracted from the main object.…”

Section: Feature Extractionmentioning

confidence: 99%

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

Aslan

2023

Balkan Journal of Electrical and Computer Engineering

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In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural Networks (CNN) methods. In the proposed method, first, image pre-processing is performed to increase the accuracy of the posterior process. The obtained image is a grayscale image without noise as a result of the pre-processing. These preprocessed images are used in classification with ML and DL. The Speeded Up Robust Features (SURF) are extracted from the grayscale image for ML-based learning. The features are restructured as visual words using the Bag of Visual Words (BoVW) method. Then, histograms are generated for each image according to the frequency of the visual word. Those histograms represent the new feature data. The histogram features are classified by four different ML methods, Decision Tree (DT), k-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). Before using the ML methods, Bayesian Optimization (BO) method, which is one of the Hyperparameter Optimization (HO) algorithms, is applied to determine hyperparameters. In the classification process performed with four different ML algorithms, the best accuracy is achieved with the KNN algorithm as 98.09%. Resnet18, ResNet50, MobileNet, GoogLeNet, DenseNet, which are state-of-the-art CNN architectures, are used for DL-based learning. CNN models have higher accuracy than ML algorithms.

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“…The point-like targets are in the complex sea and air background composed of clouds and waves and become weak small point targets in the complex environment [4]. The target point is easily submerged by noise [5]. Using a single image processing method, it is impossible to accurately track and detect the target and also to accurately grasp its moving track.…”

Section: Introductionmentioning

confidence: 99%

Correlation Filtering Algorithm of Infrared Spectral Data for Dim Target Tracking

Zheng

Chang

Wang

et al. 2023

Advances in Mathematical Physics

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The correlation filtering algorithm of infrared spectral data for dim and small target tracking is studied to improve the tracking accuracy of small and weak targets and to track small and weak targets in real time. After the image noise reduction processing by the mean shift filtering algorithm, the infrared small and weak target image data model is constructed by using the denoised infrared small and weak target image. And the brightness value and position of unknown small and weak targets are obtained. The tracking and measurement model of small and weak targets is built. The joint probabilistic data association algorithm is used to calculate the probability that each measurement is associated with its possible source targets, and the particle filter is used to update the tracking status of small and weak targets to achieve real-time tracking of small and weak targets. The experimental results show that the algorithm can enhance the edge contour information of small and weak images, so as to accurately track small and weak targets moving in any track, and has good real-time tracking and accuracy. There is a small deviation between the tracking track of weak and small targets tracked by the algorithm and the actual track, and the root mean square difference of tracking weak and small targets is within 2 pixels. In addition, the detection probability of detecting weak and small targets is less affected by SNR environmental factors.

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Study on the Influence of Image Noise on Monocular Feature-Based Visual SLAM Based on FFDNet

Cited by 16 publications

References 34 publications

Robust monocular visual inertial odometry in γ radioactive environments using edge-based point features

Robust monocular visual inertial odometry in γ radioactive environments using edge-based point features

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

Correlation Filtering Algorithm of Infrared Spectral Data for Dim Target Tracking

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