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The Gaussian Filter is similar to the mean filter however it involves a weighted average of the surrounding pixels and has a parameter sigma. new_image = cv2.blur(image,(figure_size, figure_size)) plt.figure(figsize=(11,6)) plt.subplot(121), plt.imshow(cv2.cvtColor(image, cv2.COLOR_HSV2RGB)),plt.title('Original') plt.xticks(), plt.yticks() plt.subplot(122), plt.imshow(cv2.cvtColor(new_image, cv2.COLOR_HSV2RGB)),plt.title('Mean filter') plt.xticks(), plt.yticks() plt.show()įigure 3: The result of applying a mean filter to a grayscale imageįigure 3 shows that mean filtering removes some of the noise and does not create artifacts for a grayscale image. The following is a python implementation of a mean filter: import numpy as np import cv2 from matplotlib import pyplot as plt from PIL import Image, ImageFilter %matplotlib inline image = cv2.imread('AM04NES.JPG') # reads the image image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # convert to HSV figure_size = 9 # the dimension of the x and y axis of the kernal. When dealing with color images it is first necessary to convert from RGB to HSV since the dimensions of RGB are dependent on one another where as the three dimensions in HSV are independent of one another (this allows us to apply filters to each of the three dimensions separately.)
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The blur function from the Open-CV library can be used to apply a mean filter to an image. This eliminates some of the noise in the image and smooths the edges of the image. The pixel intensity of the center element is then replaced by the mean. It involves determining the mean of the pixel values within a n x n kernel. The mean filter is used to blur an image in order to remove noise. An image from the KDEF data set (which can be found here: ) will be used for the digital filter examples.įigure 1: A 3 x 3 mean filter kernel 1. Figure 1 shows the kernel that is used for a 3 x 3 mean filter.
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The kernel depends on the digital filter. A kernal is an n x n square matrix were n is an odd number. Applying a digital filter involves taking the convolution of an image with a kernel (a small matrix). For Python, the Open-CV and PIL packages allow you to apply several digital filters. Pre-processed images can hep a basic model achieve high accuracy when compared to a more complex model trained on images that were not pre-processed. Enhancing the edges of an image can help a model detect the features of an image.Īn image pre-processing step can improve the accuracy of machine learning models. Speck noise is the noise that occurs during image acquisition while salt-and-pepper noise (which refers to sparsely occurring white and black pixels) is caused by sudden disturbances in an image signal. There are two types of noise that can be present in an image: speckle noise and salt-and-pepper noise. Image filters can be used to reduce the amount of noise in an image and to enhance the edges in an image. This article will compare a number of the most well known image filters. Image pre-processing involves applying image filters to an image. I am currently working on a computer vision project and I wanted to look into image pre-processing to help improve the machine learning models that I am planning to build.