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Statistical Preliminaries
Published in Jaakko Astola, Pauli Kuosmanen, Fundamentals of Nonlinear Digital Filtering, 2020
Jaakko Astola, Pauli Kuosmanen
There are many models for impulsive noise. For example, the impulses may have different amplitude values, or all the nonzero values are the same. Common for the models of impulsive noise in images is the appearance of noise as black and/or white spots in images, i.e., the noisy pixels have either a very small or a very large value. This type of noise is often called salt-and-pepper noise because one could create it by sprinkling salt-and-pepper on an image. Pure salt-and-pepper noise is very easy to remove from images because the maximal values occur rarely in actual images and thus just checking whether the pixel has a maximal or minimal value reveals if it is corrupted or not. A more realistic impulsive noise is modeled as bit errors in the signal values. Typical sources for this kind of noise are channel errors in communication or storage.
Training Structuring Elements in Morphological Networks
Published in Edward R. Dougherty, Mathematical Morphology in Image Processing, 2018
If an image is corrupted by salt-and-pepper noise as shown in Figure 3, then erosion by the structuring element causes the image to be completely eroded away. Dilations may cause the entire background of the image to fill in. In many cases the salt-and-pepper noise can be filtered using dilations and erosions with small structuring elements before larger probes are applied, but this is not always possible to do and still achieve the low failure rates demanded by industrial applications. For objects with a confusing background, simple erosions should be replaced by binary rank-order operators [4] (also called order statistic filters [5]) and must be trained in the context of the background.
Face Recognition–Oriented Biometric Security System
Published in Siddhartha Bhattacharyya, Anirban Mukherjee, Indrajit Pan, Paramartha Dutta, Arup Kumar Bhaumik, Hybrid Intelligent Techniques for Pattern Analysis and Understanding, 2017
Efficacy of the implemented approaches against noise variations is tested for two types of noise, namely, Gaussian and salt-and-pepper noise. To estimate the effect of noise during image acquisition due to illumination and temperature conditions, Gaussian noise of 0 mean and 0.01 variance is added in the first set of experimental test images whereas training is done on images with no added noise. To handle the modalities of data transmission, salt-and-pepper noise of density 0.05 is added to the test images in the second set of experiments. Figure 7.11 shows the face images with added Gaussian and salt-and-pepper noise.
WEL-ODKC: weighted extreme learning optimal diagonal-kernels convolution model for accurate classification of skin lesions
Published in The Imaging Science Journal, 2023
V. Auxilia Osvin Nancy, P. Prabhavathy, Meenakshi S. Arya
Data preparation includes data preprocessing, which is any sort of processing performed on raw data to prepare it for another data processing approach. Pre-processing an image is an important aspect of detection since it enhances the quality of the original image by eliminating noise. It was required to employ it in order to narrow the search for abnormalities in the background components impacting the outcome. The primary purpose of this step is to improve image quality by eliminating unnecessary and unconnected background objects prior to further processing. To eliminate the noise, we employ a median filter for denoising the salt and pepper noise added to the input image. The RGB source images were initially converted to grey to eliminate hair [31]. The black hair outlines were then found in the grayscale images using the blackHat filter. The disparity between the morphological closure process and the source images are shown by the blackHat image. The contours were then used to create the mask. The mask was reduced to simply covering the hair region, and the non-zero pixels in the source image were eliminated using an image-painting approach. The images are almost hair-free as a result of this technique. It also removes some information from the images, but the end result is relatively superior. The preprocessing operations applied to an input image are presented in Figure 3.
A novel plant leaf disease detection by adaptive fuzzy C-Means clustering with deep neural network
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Vijayaganth V, Krishnamoorthi M
It is also known as a non-linear digital filtering technique, often utilised to eliminate the noise from images. The salt and pepper noise along with the speckle noise given in the images are eliminated with this median filtering technique. The output has the same size as the nth-order one-dimensional median filter, where specifies the dimension is utilised along which the filter operation with backward compatibility. It can be utilised for replaces the provided sample with the median of the signal values. The median filtering (Gerald, 2010) denotes a non-filtering approach that is performed to remove the noise present in the images. Consider the input vector as and the output of the median filter with length is defined by , which describes the sample count. When is odd, the median filter is shown as in Equatio (1).
Breast Tumor Detection in Digital Mammogram Based on Efficient Seed Region Growing Segmentation
Published in IETE Journal of Research, 2022
Neeraj Shrivastava, Jyoti Bharti
Contrast enhancement of image is the process of remapping of image intensity value with good contrast which provides the extreme difference between black and white. The equation for contrast adjustment of pixel values can be described in Equation (1). Where the new intensity is the value of original intensity , and are the lowest and highest range of pixels and is the highest and lowest intensity pixels count. Contrast adjustment of the image is shown in Figure 1(e). Consequently, the median filter is applied to lessen the salt and pepper noise. Many authors have suggested noise reduction from MIAS and DDSM data sets [33–36]. Acute and sudden change in the image signal generates salt and pepper noise. It is viewed as small white and black spots in the image. The reduced noise image is shown in Figure 1(f). Algorithm 1 describes the steps of preprocessing of mammogram images.