Application of switching median filter with L2 norm-based auto-tuning function for removing random valued impulse noise

Bruntha, P. Malin; Dhanasekar, S.; Hepsiba, D.; Sagayam, K. Martin; Neebha, T. Mary; Pandey, Digvijay; Pandey, Binay Kumar

doi:10.1007/s42401-022-00160-y

Cited by 46 publications

(2 citation statements)

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“…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. CMF works well against salt & peppers noise but gives unsat-isfactory results against random noise.…”

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%

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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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