&lt;title&gt;Adaptive region-based filtering of multiplicative noise&lt;/title&gt;

Das, Arup; Rangayyan, Rangaraj M.

doi:10.1117/12.271138

Cited by 4 publications

(4 citation statements)

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“…The adaptive neighborhood, just like the fixed neighborhood, surrounds the pixel to be enhanced, but the shape and area covered by the adaptive neighborhood are dependent on the local characteristics of the image rather than being arbitrarily defined. Das and Rangayyan [18] extended the method to multiplicative noise. Das and Rangayyan [18] extended the method to multiplicative noise.…”

Section: Adaptive Neighborhood Filteringmentioning

confidence: 99%

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Adaptive neighborhood filters for color image filtering

Ciuc

Rangayyan

Zaharia³

et al. 1998

Nonlinear Image Processing IX

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Various nonlinear, fixed-neighborhood techniques for filtering color images based on local statistics have been proposed in the literature. We present adaptive neighborhood filtering (ANF) techniques for noise removal in color images. The main idea is to find for each pixel in the image (called the "seed" when being processed) a variable-shaped, variable-sized neighborhood that contains only pixels that are similar to the seed. Then, statistics computed using pixels within the adaptive neighborhood are used to derive the filter output.Results of the ANF techniques are compared with those given by a few multivariate fixed-neighborhood filters: the double-window modified trimmed-mean filter, the generalized vector directional filter -doublewindowcr-trimmed mean filter, the adaptive hybrid multivariate filter, and the adaptive non-parametric filter with Gaussian kernel. It is shown that the ANF techniques provide the best visual results, effectively suppressing noise while not blurring edges. The ANF results are also the best in terms of objective measures ( such as normalized mean-squared error and normalized color difference).

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Section: Adaptive Neighborhood Filteringmentioning

confidence: 99%

“…Application of adaptive neighborhood filters developed for gray-scale images [16, 17,18] to the case of color (multichannel) images forms the focus of the present paper. Application of adaptive neighborhood filters developed for gray-scale images [16, 17,18] to the case of color (multichannel) images forms the focus of the present paper.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neighborhood filters for color image filtering

Ciuc

Rangayyan

Zaharia³

et al. 1998

Nonlinear Image Processing IX

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“…U-Net has been a remarkable and the most popular deep network architecture, and it is introduced into the change detection in this paper [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Neighborhood analysis is introduced to look for the corresponding image points of two-time images (potentially distorted images) and to find their difference [33][34][35][36][37][38][39][40][41][42]. The method of circle neighborhood analysis reduces the error of constructing the difference image, and improves the precision of the U-Net.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Robust Change Detection Approach for Very High Resolution Remotely Sensed Images with Multiple Features

Huang

Zhao

et al. 2020

Remote Sensing

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Very high-resolution remote sensing change detection has always been an important research issue due to the registration error, robustness of the method, and monitoring accuracy, etc. This paper proposes a robust and more accurate approach of change detection (CD), and it is applied on a smaller experimental area, and then extended to a wider range. A feature space, including object features, Visual Geometry Group (VGG) depth features, and texture features, is constructed. The difference image is obtained by considering the contextual information in a radius scalable circular. This is to overcome the registration error caused by the rotation and shift of the instantaneous field of view and also to improve the reliability and robustness of the CD. To enhance the robustness of the U-Net model, the training dataset is constructed manually via various operations, such as blurring the image, increasing noise, and rotating the image. After this, the trained model is used to predict the experimental areas, which achieved 92.3% accuracy. The proposed method is compared with Support Vector Machine (SVM) and Siamese Network, and the check error rate dropped to 7.86%, while the Kappa increased to 0.8254. The results revealed that our method outperforms SVM and Siamese Network.

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“…Extension of adaptiveneighborhood filters developed for gray-scale images [15][16][17][18][19] to the case of color ͑multichannel͒ images 20 forms the focus of the present paper.…”

Section: Introductionmentioning

confidence: 99%

Filtering noise in color images using adaptive-neighborhood statistics

Ciuc

Rangayyan

Zaharia

et al. 2000

J. Electron. Imaging

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Various nonlinear, fixed-neighborhood techniques based on local statistics have been proposed in the literature for filtering noise in color images. We present adaptive-neighborhood filtering (ANF) techniques for noise removal in color images. The main idea is to find for each pixel (called the ''seed'' when being processed) a variable-shaped, variable-sized neighborhood that contains only pixels that are similar to the seed. Then, statistics computed within the adaptive neighborhood are used to derive the filter output. Results of the ANF techniques are compared with those given by a few multivariate fixed-neighborhood filters: the double-window modified trimmed-mean filter, the generalized vector directional filterdouble-window-␣-trimmed mean filter, the adaptive hybrid multivariate filter, and the adaptive nonparametric filter with Gaussian kernel. It is shown that the ANF techniques provide better visual results, effectively suppressing noise while not blurring the edges; the results are also better in terms of objective measures (such as normalized mean-squared error and normalized color difference) than the results of the other methods.

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<title>Adaptive region-based filtering of multiplicative noise</title>

Cited by 4 publications

References 0 publications

Adaptive neighborhood filters for color image filtering

Adaptive neighborhood filters for color image filtering

A Deep Learning-Based Robust Change Detection Approach for Very High Resolution Remotely Sensed Images with Multiple Features

Filtering noise in color images using adaptive-neighborhood statistics

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