Digital Image Processing refers to using computer algorithms to process digital images for improving quality or to extract information. With the spurt of growth that image analytics is witnessing currently, Digital Image Processing is a handy tool for data analysts to have in their repertoire. This article aims at outlining the quintessential techniques used as a part of Digital Image Processing that is quickly gaining traction amongst data analysts.
The smallest unit of an image, commonly referred to as pixels, represent a list of values that corresponds to the colour model of the image. For example each pixel of the RGB(Red,Green,Blue) colour model would specify a list of 3 values corresponding to intensity of the colors Red, Green and Blue present in the pixel.
To get started with, it is important to know of the few commonly used methods of Digital Image Processing. In this article aims at giving you a bird’s eye view of some of the essential methodologies.
Noise filtering – Noise refers to the unwanted or unnecessary information that is present in an image. Usually, we can find a underlying pattern to the extra unwanted information. Commonly occuring noise patterns of images include Gaussian noise (noise that has a normal distribution), salt and pepper noise (a bright pixels in dark region or a dark pixels in bright region), periodic noise(noise caused by electrical or electromechanical interference). Once a pattern is found we can remove the noise by altering the pixel values.
Average Blurred Image
Contrast enhancement – Contrast enhancement brings out the difference in image pixel values and helps in identifying the parts. Multiple ways of contrast enhancement exists in digital image processing in which image-adjust(increases the highest values and decreases the lowest values to predefined values), Histogram equalization(spreading the peaks and lows in image histogram to spread evenly in the image) are few algorithms commonly used.
Average Blurred Image
Image segmentation – Segmentation can be described as a process of partitioning image into groups which helps in identifying patterns, and studying objects in the image. There are many instances wherein segmentation of an image becomes important. For example, separating the image of a human bone into outer and inner layers can help in identifying a fracture location thoroughly.
Commonly used methods in image segmentation include channel separation, thresholding methods and more. Channel separation involves converting an image into separate channel of base colour model. Thresholding methods divide the image into parts by defining a threshold value which can be obtained by studying image histograms or by dynamically focusing on a region of the image and statistically defining a value by looking at the average value in the focused area. Weighted average of pixels or median value of the focused area can be used as well. Thresholding creates binary images with each pixel representing only one of two distinct values, in which all the parts of importance are set as one static value rest as another static value.
Edge and Contour detection – Edge is a point in image representing sudden and abrupt change in the pixel values which represents occurence of a event like start or end of a object in image. Edges are obtained by finding the local derivative of the distribution of colour model. The existence of noise however, will result in finding irrelevant or wrong points being detected. So we need to employ processing methods like noise removal and blurring of images to remove the unnecessary or wrong gradients from image. Contours are curves obtained by tracing the continuous edge points of the same intensity, through the obtained contours we can extract the information about the region such as dimensions of the region and we can use the obtained region as region of interest for machine learning activities like object classification and/or tracking.
In a world that is now producing information in multiple media channels, aspects like Digital Image Processing take on an increasingly vital role in rendering effective analytics. Getting the foundation of it right by understanding and perfecting the basic techniques involved is thus imperative to anyone attempting to make headway into the world of image analytics.