Digital Image Processing

What is Digital Image Processing?

Digital image processing is an advanced technology that enables you to manipulate digital images through computer software. It is the subfield of signal processing, which focuses primarily on images. Digital image processing allows the user to take the digital image as an input and perform the different algorithm on it to generate an output. These algorithms may vary from image to image according to the desired output image. Adobe Photoshop is the most popular software that uses digital image processing to edit or manipulate images.

One of the key benefits of integrating regularly is that you simply can detect errors quickly and locate them more easily. As each change introduced is usually small, pinpointing the precise change that introduced a defect is often done quickly.

The three primary phases that constitute image processing are:

Importing the image using picture acquisition tools
Picture processing and handling
Output in which image or report based on image analysis may be changed

Types of Image:

An image is a 2D array of numeric values called pixels. These pixels carry a value that indicates the amount of light for that particular pixel. The pixel carry value represents information about the number of intensities present in the image. The value 0 represents that color black, the value 1 represents the color white. The images are further characterized in two types

Greyscale image

The color grey lies within the white and black range. The images whose pixels carry values between 0 to 1 are characterized as greyscale images.

RGB Image

RGB stands for Red, Green, and Blue. Any other color can be derived from these three primary colors. Each pixel of a colored image carries different 16 or 24-bit color values. These 16 or 24 bits are further divided into three values that correspond to the RGB values. The combination of the RGB forms the exact colors of the pixel.

Basic functions of Digital Image Processing

Here are some basic functions that can be performed on an image, which will change the characteristic of the image.

Image enhancement

The image enhancement function uses its algorithms to improve image features. It adjusts the image in such a form that all the results are more suitable for further analysis. Also, this helps the user to extract hidden features by using techniques like sharpening, curves, etc. The function improves the visualization, removes the unwanted parts, deblurs the image, and much more.

Noise addition

Noise is the unwanted parts of the image. The noise is added to an image for testing purposes, it helps the user to test the efficiency of the noise removal filters. There are different types of noises, some of which are listed as follows

Salt and pepper noise

Massive and abrupt fluctuations in the image signal may cause this noise. This is mostly white with black pixels. The white pixels are termed as salt and pepper as black pixels in the image.

Gaussian noise

Gaussian noise is the statistical noise with a probability density function (PDF). This type of noise is used to mimic many random natural processes like high temperature, transmission, and poor illumination.

Speckle noise

An active radar sensor gives off a burst of coherent radiation, which reflects from the target, unlike a passive microwave sensor, which simply receives the low-level radiation naturally emitted by targets. The waves emitted by the active sensors are in phase unless they strike a target. Upon interaction with the target, these waves get out of phase. These out of phase waves interacts to produce a mix of light and dark pixel in an image. This disturbance is referred to as the Speckle Noise.

Motion blur

Motion blur is the most well-known type of noise. It is caused in the image due to the scattering of pixel because of camera motion while taking the picture. This blur can also be added to an image using the image processing techniques.

Filters

In digital image processing, filters are used to perform a different function on the image, such as removing noise, enhancing the image, detecting edges, and much more. There are different types of noises, and they require different filters to remove them; some of them are

Median filter

Median filters are handy for removing random noise from the image. This filter is very effective for the noise, which has a significant tails noise amplitude probability and periodic patterns like salt and pepper noise. Unlike low pass filters and high pass filters, the median filter does not require a mask. This filter solely depends upon the mathematical concept of the median.
In order to apply the median filter, a window slides throughout the image. After that, the median of all the pixels is calculated using the standard mathematical formula of the median. The calculated median replaces the pixel at the center of the sliding window. This process is repeated on the whole image to obtain the median filtered image.

Laplacian filter

The Laplacian is a second derivative filter which is used to find the edges in an image. The Laplacian image highlights rapid intensity-change in images; these areas are usually the edges. These kinds of filters are susceptible to the noise, so in order to apply this filter, one must smoothen the image. In most cases, the smoothing of the image is done using the Gaussian filter, and after that, the Laplacian filter is applied.

Gaussian filter

A Gaussian filter is a filter whose impulse response gives you an approximation of a Gaussian function. Gaussian filters have the potential to neglect a phase feature while reducing the noise. It is a linear filter which is also used for preprocessing in the edge detection. It is even faster than the median filter due to the less complexity in the equation.

Applications:

Image processing is serving a key role in many industries like medical, computer vision, and AI. Clinical imaging is the method used in the medical field to diagnose or study diseases by acquiring photographs of body parts. Millions of image tests are carried out around the world for research purposes. Advances image processing techniques, including image recovery, analysis, and enhancing, have helped a lot in the evolution of medical imaging.
In computer vision and AI, image processing techniques are used to extract the hidden features of the image. This is very effective in processes like image recognition and facial recognition. Other than that,

digital signal processing

is a vital part of the industries related to showbiz. All the alternation made in an image, using software like Photoshop, is the part of image processing.