Restoration method for blurred star images based on region filters

Jun, 王军 Wang; Xin, 何昕 He; Zhong-hui, 魏仲慧 Wei; Zhi-ya, 穆治亚 Mu; You, 吕 游 Lv; Jia-wei, 何家维 He

doi:10.3788/co.20191202.0321

Cited by 3 publications

(2 citation statements)

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“…Table 6 shows that the iteration time of the improved algorithm was the lowest. Although the PSNR of the improved algorithm was 0.7 dB less than that of the KWFLICM algorithm [44,45], the iteration time was 300 s less than that of the KWFLICM algorithm, and the PSNR of the brain CT image segmentation test results in Table 6 was 0.7 dB less than that of the KWFLICM algorithm. However, the iteration time was 45 s less than that of the KWFLICM algorithm.…”

Section: Test Resultsmentioning

confidence: 99%

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

Ren

2020

Sensors

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Since the fuzzy local information C-means (FLICM) segmentation algorithm cannot take into account the impact of different features on clustering segmentation results, a local fuzzy clustering segmentation algorithm based on a feature selection Gaussian mixture model was proposed. First, the constraints of the membership degree on the spatial distance were added to the local information function. Second, the feature saliency was introduced into the objective function. By using the Lagrange multiplier method, the optimal expression of the objective function was solved. Neighborhood weighting information was added to the iteration expression of the classification membership degree to obtain a local feature selection based on feature selection. Each of the improved FLICM algorithm, the fuzzy C-means with spatial constraints (FCM_S) algorithm, and the original FLICM algorithm were then used to cluster and segment the interference images of Gaussian noise, salt-and-pepper noise, multiplicative noise, and mixed noise. The performances of the peak signal-to-noise ratio and error rate of the segmentation results were compared with each other. At the same time, the iteration time and number of iterations used to converge the objective function of the algorithm were compared. In summary, the improved algorithm significantly improved the ability of image noise suppression under strong noise interference, improved the efficiency of operation, facilitated remote sensing image capture under strong noise interference, and promoted the development of a robust anti-noise fuzzy clustering algorithm.

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Section: Test Resultsmentioning

confidence: 99%

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

Ren

2020

Sensors

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“…The remote sensing images of wheat fields, canyons and forests had multiplicative noise added with a mean value of 0 and mean squared deviations of 90, 121 and 61, respectively [50][51][52]. The numbers of clusters were 2, 2 and 3, respectively.…”

Section: Image Segmentation Test With Multiplicative Noisementioning

confidence: 99%

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

Ren

2020

Sensors

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This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback–Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means (FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model (SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272–12.9803 dB, 1.5501–13.4396 dB, 1.9113–11.2613 dB and 1.0233–10.2804 dB over the other methods, and the Misclassification rate (MSR) decreases by 0.32–37.32%, 5.02–41.05%, 0.3–21.79% and 0.9–30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation.

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A size-adaptive strategy to characterize spatially heterogeneous neighborhood effects in cellular automata simulation of urban growth

Bin

Wang

et al. 2023

Landscape and Urban Planning

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Restoration method for blurred star images based on region filters

Cited by 3 publications

References 0 publications

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

A size-adaptive strategy to characterize spatially heterogeneous neighborhood effects in cellular automata simulation of urban growth

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