De-noising of images from salt and pepper noise using hybrid filter, fuzzy logic noise detector and genetic optimization algorithm (HFGOA)

Selvi, A. Senthil; Kumar, K. Pradeep Mohan; Dhanasekeran, S.; Maheswari, P. Uma; Ramesh, S.; Pandi, S. Senthil

doi:10.1007/s11042-019-7727-9

Cited by 23 publications

(6 citation statements)

References 21 publications

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“…The techniques explained above are essential part of most of the impulse noise removal filters. Fuzzy logic based pixel density based models [19,20], reasoning model [22], switching [23], directional [24], clustering model [25], computation models [27] and hybrid filter [28] use generic linear and non-linear statistical methods which may produce good results under certain conditions due to non-linear nature of the median filter but generality is not true. Cluster based median filter (CMF) [25], region adaptive filter (RAF) [26], new weighted mean (NWM) filter [29] and others [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][30][31][32][33][34] mainly use above said criteria's in noise detection phases.…”

Section: Deviation From a Reference Pointmentioning

confidence: 99%

“…All of the above filters are efficient against impulse noise but fail due to blurring [11][12][13] at edges and loss of actual details in an image. Switching mechanisms [9,17], fuzzy based techniques [19][20][21][22][23][24][25][26][27][28], directional filters [15,16,22,24] and others [29][30][31][32][33][34][35][36][37][38][39] are good de-noising filters against random and universal noise but still lacking in detail preservation due to poor or no proper edge detection.…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy logic has added excellent noise removal capabilities against different types of noise due to uncertainty features [19][20][21][22][23][24][25][26][27][28]32]. A fuzzy logic based filtering mechanism is proposed in [19] for additive noise, which is mainly, based on fuzzy image derivatives in eight directions.…”

Section: Introductionmentioning

confidence: 99%

“…Hussain et al in [24,25] proposed fuzzy reasoning based clustering criteria of pixels to address the problems in above filters. Similar fuzzy logic based other filters [26][27][28]32] address neighboring issues and hybrid approaches for effective impulse noise removal.…”

Section: Introductionmentioning

confidence: 99%

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Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising

et al. 2023

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In this paper, a convolved feature vector based adaptive fuzzy filter is proposed for impulse noise removal. The proposed filter follows traditional approach, i.e., detection of noisy pixels based on certain criteria followed by filtering process. In the first step, proposed noise detection mechanism initially selects a small layer of input image pixels, convolves it with a set of weighted kernels to form a convolved feature vector layer. This layer of features is then passed to fuzzy inference system, where fuzzy membership degrees and reduced set of fuzzy rules play an important part to classify the pixel as noise-free, edge or noisy. Noise-free pixels in the filtering phase remain unaffected causing maximum detail preservation whereas noisy pixels are restored using fuzzy filter. This process is carried out traditionally starting from top left corner of the noisy image to the bottom right corner with a stride rate of one for small input layer and a stride rate of two during convolution. Convolved feature vector is very helpful in finding the edge information and hidden patterns in the input image that are affected by noise. The performance of the proposed study is tested on large data set using standard performance measures and the proposed technique outperforms many existing state of the art techniques with excellent detail preservation and effective noise removal capabilities.

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Section: Deviation From a Reference Pointmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising

et al. 2023

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“…Deep learning network plays vital role in all area of research such as food adulteration [53], noise removal [54], mobile phone recycling process [55], and sign language understanding [56]. The optimization algorithm provides an optimized solution in all area of researches such as noise removal [57,58], micro-grid protection [59], food adulteration [60], etc.…”

Section: Related Workmentioning

confidence: 99%

A Bio-Inspired Method For Breast Histopathology Image Classification Using Transfer Learning

R K Chandana Mani,

J Kamalakannan,

Y. Pandu Rangaiah

et al. 2023

JAIT

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Breast cancer is one of the deadly cancer among the female population, and still a developing area of research in the field of medical imaging. The fatality rate is more in patients who are not early diagnosed and are given delayed treatment. Hence researchers are keeping their lot of efforts in developing breast cancer detection systems that could provide accurate diagnosis in the initial stages which are relied on medical imaging. Deep learning is offering key solutions to overcome many image classification tasks. Though deep learning techniques have extended their root to many medical fields even it suffers from the problem of lack of sufficient data. Convolutional Neural Networks are more preferred for medical image classification tasks. In this paper, we propose a transfer learning method that overcomes the issue of insufficient data. We use a familiar pre-trained network VGG-16 (Visual Geometric Group) + with Logistic Regression as a binary classifier. Since hyper-parameters of every CNN has a closer impact on the performance of the entire deep learning model, our method focus on optimizing hyper-parameters using particle swarm optimization which is a bio-inspired algorithm, The proposed model performs classification of Breast Histopathology images into benign and malignant images and produce better results. We use Break His Dataset to test our method and achieve an accuracy of around 96.9%. The experimental results show that this study has improved performance metrics when compared to other transfer learning methods.

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