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

Abstract

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).

Automated fiducial marker detection and localization in volumetric computed tomography images: a three-step hybrid approach with deep learning

“…Also, the TT is very computationally expensive in grayscale compared to binary morphology as the pixel values (e.g., finding the minimum and maximum) are compared for integers, one pixel at a time. The binary opening operation for noise reduction is optimized with a sequence of grayscale median filters or better computational performance 28 —a binary dilation followed by a sequence of binary erosions (binary closing). This optimization will better preserve the original image data and is much less sensitive to discretization effects occurring in large voxel sizes (e.g., ) or in smaller markers (e.g., tiny surgical screws).…”

New Real-Time High-Density Impulsive Noise Removal Method Applied to Medical Images

“…This reduces the effect of impulsive noise while preserving other image features. Another option is to use a non-linear filter, such as a morphological filter [6][7][8][9]. This smooths the edges of objects while preserving their shapes and is effective at removing impulsive noise.…”