A Novel Profuse Clustering Technique for Image Denoising

Thamilselvan, P; Sathiaseelan, J. G. R.

doi:10.1016/j.procs.2017.12.019

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Image filtering is a fundamental skill in image processing. There are many literatures of this topic and representative approaches include wavelet transform [13], adaptive total variation [14], clustering learning [15] and low rank learning [16], etc. Recently, non-local methods, such as NLM [17] and BM3D [18], have achieved promising results in image denoising by capturing the similarity between patches within a broader context.…”

Section: Related Workmentioning

confidence: 99%

Window‐aware guided image filtering via local entropy

Liu

Yang

Wang

2021

IET Image Processing

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Guided image filtering is one of the widely used techniques in computer vision. However, it commonly leads to over‐smoothed edges and a distorted appearance when tackling intricate texture patterns and complex noise. In this paper, a window‐aware image filtering framework based on the bilateral filter guided by the local entropy is presented. The key idea of the authors' proposed approach is to design a novel guidance input and a non‐box filtering window. Specifically, using the Gaussian spatial kernel and the local entropy, a GEF that can maintain image feature details and yield a robust guidance input for BF is constructed. Meanwhile, based on an intensity‐similar strategy, the local non‐box filtering window is designed for the further preservation of edge structures. The authors' approach not only inherits the advantages of bilateral filter i.e. simplicity, parallelisation and easiness of programming, but also is more powerful than bilateral filter and its variants. In addition, the guided entropy filter and the non‐box window can also be transplanted to other local filters and can effectively improve the filtering effects. The qualitative and quantitative experimental results demonstrate that the authors' approach has good performance in image denoising, texture (or background) smoothing, edge extraction and other applications in image processing.

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Section: Related Workmentioning

confidence: 99%

Window‐aware guided image filtering via local entropy

Liu

Yang

Wang

2021

IET Image Processing

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Color image restoration by filtering methods: a review

Salamat,

Missen,

Akhtar

et al. 2024

Soft Comput

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Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks.

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Models for Internal Clustering Validation Indexes Based on Hadoop-MapReduce

Zerabi

Meshoul

Boucherkha

2020

International Journal of Distributed Systems and Technologies

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Cluster validation aims to both evaluate the results of clustering algorithms and predict the number of clusters. It is usually achieved using several indexes. Traditional internal clustering validation indexes (CVIs) are mainly based in computing pairwise distances which results in a quadratic complexity of the related algorithms. The existing CVIs cannot handle large data sets properly and need to be revisited to take account of the ever-increasing data set volume. Therefore, design of parallel and distributed solutions to implement these indexes is required. To cope with this issue, the authors propose two parallel and distributed models for internal CVIs namely for Silhouette and Dunn indexes using MapReduce framework under Hadoop. The proposed models termed as MR_Silhouette and MR_Dunn have been tested to solve both the issue of evaluating the clustering results and identifying the optimal number of clusters. The results of experimental study are very promising and show that the proposed parallel and distributed models achieve the expected tasks successfully.

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A Novel Profuse Clustering Technique for Image Denoising

Cited by 3 publications

References 9 publications

Window‐aware guided image filtering via local entropy

Window‐aware guided image filtering via local entropy

Color image restoration by filtering methods: a review

Models for Internal Clustering Validation Indexes Based on Hadoop-MapReduce

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