Salt and Pepper Noise Removal Algorithm by Novel Morpho Filter

Priya, K. Sindhu; Pugazhenth, D.

doi:10.3923/jas.2014.950.954

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Thin lines can be erased by using a small circular kernel for erosion, similar to impulse noise [22] and any lost information can be restored using the same kernel and dilation.…”

Section: Stationary Noise Reductionmentioning

confidence: 99%

Video Action Recognition Using Depth Motion Maps

Tan

2024

Preprint

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<p>This thesis presents an algorithm for modifying Depth Motion Map (DMM) pyramid analysis based action recognition. It extends the work presented by Chengwu Liang to better handle incoming data. Morphological image transforms are used to remove incoming noise created by the inaccuracies in a Microsoft Kinect when the subject is stationary. This avoids the problem of motion being registered in frames where there is no motion. It also uses a non-linear cost metric on the motion energy to ensure some degrees of shift invariance in the Z axis, which is towards or away from the camera. In addition, advanced information fusion methods are adopted to improve feature representation, leading to better performance. Experiments are conducted on video data containing a variable number of stationary frames at the start and end of an action. The experiments are conducted with many different classifiers. The results show that a performance benefit is realized due to the use of these enhancement methods.</p>

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“…Thin lines can be erased by using a small circular kernel for erosion, similar to impulse noise [22] and any lost information can be restored using the same kernel and dilation.…”

Section: Stationary Noise Reductionmentioning

confidence: 99%

Video Action Recognition Using Depth Motion Maps

Tan

2024

Preprint

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“…This is the different one from filtering in which the noise is filtered with the help of filter and the output image is obtained. But here, a kind of image analysis is performed to the given input image (Priya and Pugazhenthi, 2014).…”

Section: Morphological Image Filteringmentioning

confidence: 99%

A Case Study of Impulse Noise Reduction Using Morphological Image Processing with Structuring Elements

Elamaran¹,

Upadhyay²,

Narasimhan³

et al. 2015

Asian J. of Scientific Research

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Image enhancement plays a vital role in the field of digital image processing since the noise is added very often with the original image. Spatial filtering techniques like low pass, high pass, band pass and notch with the help of convolution mask are often used to enhance the image with reduced noise. Recently, morphological algorithms play a major role in the area of filtering noise, boundary detection, shape detection, image manipulation, etc. Especially by applying dilation, erosion, opening and closing to the image appropriately, the quality of the image can be further enhanced. In this study, morphological algorithms are being applied to remove the salt and pepper noise from the input image. Erosion followed by dilation and dilation followed by erosion are the main methods to remove this kind of impulsive noise. MATLAB software is used to apply these opening and closing methods to enhance the quality of the image with difference structuring elements. Simulation results are obtained and the comparison is done with the performance metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Error (MAE), Normalized Correlation Coefficient (NCC) and Image Enhancement Factor (IEF) with different structuring elements.Key words: Image enhancement, closing, impulse noise, morphological filtering, opening INTRODUCTIONIn the field of image processing, modern trend moves towards handling images effectively with more clarity and high performance. This image enhancement can be done either in spatial domain or in frequency domain. Low pass, high pass, band pass and notch filters are designed to remove the noise from the image or to enhance the quality of the image (Chen et al., 2013). If the filter or kernel size is small, filtering can be done more effectively in the spatial domain (Islam et al., 2010). If the kernel or convolution mask size becomes large, this would be tedious in the spatial domain. In such a case, the filtering could be done more effectively in the frequency domain. Since the convolution becomes multiplication here, the processing would be done more effectively even with large kernel size (Elamaran et al., 2013;Malik and Baharudin, 2012).In some applications, there is a necessity to detect the shape and structure of the image or a pattern within an image. Morphological algorithms are more pertinent for this kind of image analysis (Li et al., 2013). A particular shape can be extracted and analyzed with the help of dilation and erosion processes. A particular pattern size can be enlarged by dilation and minimized by erosion. By combining both dilation and erosion, morphological filtering would be achieved. Input image can be smoothened along with the removal of noise by opening and closing. Edge detection, feature detection, image segmentation, counting objects is the other applications of morphological algorithms. These operations are not linear like spatial filtering using convolution masks (Elamaran et al., 2012;Dougherty, 2009).

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Salt and Pepper Noise Removal Algorithm by Novel Morpho Filter

Cited by 2 publications

References 9 publications

Video Action Recognition Using Depth Motion Maps

Video Action Recognition Using Depth Motion Maps

A Case Study of Impulse Noise Reduction Using Morphological Image Processing with Structuring Elements

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