Urban vegetation segmentation using terrestrial LiDAR point clouds based on point non-local means network

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

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Sparse tomographic image reconstruction method using total variation and non-local means

2015 IEEE International Conference on Imaging Systems and Techniques (IST) ◽

10.1109/ist.2015.7294554 ◽

2015 ◽

Author(s):

Metin Ertas ◽

Aydin Akan ◽

Isa Yildirim ◽

Mustafa Kamasak

Keyword(s):

Image Reconstruction ◽

Total Variation ◽

Reconstruction Method ◽

Tomographic Image ◽

Tomographic Image Reconstruction ◽

Local Means ◽

Image Reconstruction Method ◽

Non Local

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Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18062903 ◽

2021 ◽

Vol 18 (6) ◽

pp. 2903

Author(s):

Seong-Hyeon Kang ◽

Ji-Youn Kim

Keyword(s):

Light Microscopy ◽

Noise Reduction ◽

Natural Image ◽

Search Window ◽

Local Means ◽

Image Characteristics ◽

Non Local ◽

Spatial Quality ◽

Evaluation Parameters ◽

Color Channels

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

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MEDICAL IMAGE DE-NOISING USING NON LOCAL MEANS FILTERING

Journal of Xidian University ◽

10.37896/jxu15.5/042 ◽

2021 ◽

Vol 15 (5) ◽

Keyword(s):

Medical Image ◽

Local Means ◽

Non Local

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Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

International Journal of Image and Graphics ◽

10.1142/s0219467821500510 ◽

2021 ◽

pp. 2150051

Author(s):

S. Elavaar Kuzhali ◽

D. S. Suresh

Keyword(s):

Image Denoising ◽

Image Patch ◽

The Past ◽

Local Means ◽

Hybrid Optimization Algorithm ◽

Proposed Model ◽

Processing Step ◽

Non Local ◽

Chicken Swarm Optimization ◽

Selection Of

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

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