An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

Basar, Sadia; Waheed, Abdul; Ali, Mushtaq; Zahid, Saleem; Zareei, Mahdi; Biswal, Rajesh Roshan

doi:10.3390/s22072724

Cited by 14 publications

(9 citation statements)

References 71 publications

(145 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Fig 1 , the image processing is mainly divided into three parts, including image input, preprocessing, and segmentation [ 17 ]. Image processing covers photo conversion and digitization of image input, preprocessing, and image segmentation using techniques such as thresholding, edge detection, and regioning.…”

Section: Application Of Mcmc Algorithm Based On Mrf In Image Segmenta...mentioning

confidence: 99%

MCMC algorithm based on Markov random field in image segmentation

Wang,

2024

PLoS ONE

View full text Add to dashboard Cite

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.

show abstract

Section: Application Of Mcmc Algorithm Based On Mrf In Image Segmenta...mentioning

confidence: 99%

MCMC algorithm based on Markov random field in image segmentation

Wang,

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Clustering analysis is an unsupervised learning technique that seeks interesting patterns in datasets without pre-existing true class labels. Since the true class labels are unknown, and hence the true structure can not be predicted in the dataset [26,27]. To overcome this limitation, we use the simulated datasets to achieve the study objectives.…”

Section: Data Generationmentioning

confidence: 99%

The least sample size essential for detecting changes in clustering solutions of streaming datasets

Atif,

Farooq,

Abiad

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

The clustering analysis approach treats multivariate data tuples as objects and groups them into clusters based on their similarities or dissimilarities within the dataset. However, in modern world, a significant volume of data is continuously generated from diverse sources over time. In these dynamic scenarios, the data is not static but continually evolves. Consequently, the interesting patterns and inherent subgroups within the datasets also change and develop over time. The researchers have paid special attention to monitoring changes in cluster solutions of evolving streams. For this matter, several algorithms have been proposed in the literature. However, to date, no study has examined the effect of variability in cluster sizes on the evolution of cluster solutions. Moreover, no guidance is available on determining the impact of cluster sizes on the type of changes they experience in the streams. In the present simulation study using artificial datasets, the evolution of clusters is examined concerning the variability in cluster sizes. The findings are substantial because tracing and monitoring the changes in clustering solutions have a wide range of applications in every field of research. This study determines the minimum sample size required in the clustering of time-stamped datasets.

show abstract

“…The stimulus is received by the feeding input field and fed back to the firing subsystem through modulation field. [61]. Reprinted/adapted with permission from Ref.…”

Section: Pulse Coupled Neural Networkmentioning

confidence: 99%

A Survey of Multi-Focus Image Fusion Methods

Zhou

Zhi

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the methods based on boundary segmentation was put forward as a group of image fusion method. Thus, a novel classification method of image fusion algorithms is proposed: transform domain methods, boundary segmentation methods, deep learning methods, and combination fusion methods. In addition, the subjective and objective evaluation standards are listed, and eight common objective evaluation indicators are described in detail. On the basis of lots of literature, this paper compares and summarizes various representative methods. At the end of this paper, some main limitations in current research are discussed, and the future development of multi-focus image fusion is prospected.

show abstract

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

Cited by 14 publications

References 71 publications

MCMC algorithm based on Markov random field in image segmentation

MCMC algorithm based on Markov random field in image segmentation

The least sample size essential for detecting changes in clustering solutions of streaming datasets

A Survey of Multi-Focus Image Fusion Methods

Contact Info

Product

Resources

About