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<a href="#" id="menu-submit"><span></span>Menu</a></div></div></div></div></div></div></div></header><main id="main" role="main"><div class="section-wrapper cf"><div class="section-wrapper-content cf"><section class="section default-01 design-01 wsection-white"><div class="section-bg"><div class="section-bg-layer"></div><div class="section-bg-layer section-bg-overlay"></div></div><div class="section-inner"><div class="content cf wnd-no-cols"><div><div class="text cf design-01"><div class="container"><div class="vc_row vc_row-fluid boxed"><div class="wpb_column vc_column_container vc_col-sm-12"><div class="vc_column-inner "><div class="wpb_wrapper"><div class="wpb_text_column wpb_content_element " ><div class="wpb_wrapper"><h1 style="text-align: center;"><span style="color: #ff0000;">Digital Image Processing</span></h1><h3 style="text-align: left;"><span style="color: #000000;">What is Digital Image Processing?</span></h3><p><span style="font-weight: 400;">Digital image processing is an advanced <a href="https://www.ssla.co.uk/nanotechnology/">technology</a> 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 <a href="https://www.ssla.co.uk/software-engineering/">software</a> that uses digital image processing to edit or manipulate images.</span></p><p><span style="font-weight: 400;">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.</span></p><p><span style="font-weight: 400;">The three primary phases that constitute image processing are:</span></p><ul><li>Importing the image using picture acquisition tools</li><li>Picture processing and handling</li><li>Output in which image or report based on image analysis may be changed</li></ul><h3 style="text-align: left;"><span style="color: #000000;">Types of Image:</span></h3><p><span style="font-weight: 400;"><br />
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<br />
</span></p><h4 style="text-align: left;"><span style="color: #000000;">Greyscale image</span></h4><p><span style="font-weight: 400;"><br />
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.<br />
</span></p><h4 style="text-align: left;"><span style="color: #000000;">RGB Image</span></h4><p><span style="font-weight: 400;"><br />
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.<br />
</span></p><p><img data-lazyloaded="1" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIzMDAiIGhlaWdodD0iMTE1IiB2aWV3Qm94PSIwIDAgMzAwIDExNSI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSIgc3R5bGU9ImZpbGw6I2NmZDRkYjtmaWxsLW9wYWNpdHk6IDAuMTsiLz48L3N2Zz4=" decoding="async" class="alignnone wp-image-7440 size-medium" data-src="https://www.ssla.co.uk/wp-content/uploads/2020/08/typesofimages-300x115.png" alt="digital image processing basics" width="300" height="115" data-srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/typesofimages-300x115.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/08/typesofimages-600x230.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/08/typesofimages.png 634w" data-sizes="(max-width: 300px) 100vw, 300px" /><noscript><img decoding="async" class="alignnone wp-image-7440 size-medium" src="https://www.ssla.co.uk/wp-content/uploads/2020/08/typesofimages-300x115.png" alt="digital image processing basics" width="300" height="115" srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/typesofimages-300x115.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/08/typesofimages-600x230.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/08/typesofimages.png 634w" sizes="(max-width: 300px) 100vw, 300px" /></noscript></p><h3 style="text-align: left;"><span style="color: #000000;">Basic functions of </span><span style="color: #000000;">Digital Image Processing</span></h3><p><span style="font-weight: 400;"><br />
Here are some basic functions that can be performed on an image, which will change the characteristic of the image.<br />
</span></p><h4 style="text-align: left;"><span style="color: #000000;">Image enhancement</span></h4><p><span style="font-weight: 400;"><br />
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.<br />
</span></p><h4 style="text-align: left;"><span style="color: #000000;">Noise addition</span></h4><p><span style="font-weight: 400;"><br />
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<br />
</span></p><h5 style="text-align: left;"><span style="color: #000000;">Salt and pepper noise</span></h5><p><span style="font-weight: 400;"><br />
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.<br />
</span><br />
<img data-lazyloaded="1" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyOTgiIGhlaWdodD0iMzAwIiB2aWV3Qm94PSIwIDAgMjk4IDMwMCI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSIgc3R5bGU9ImZpbGw6I2NmZDRkYjtmaWxsLW9wYWNpdHk6IDAuMTsiLz48L3N2Zz4=" fetchpriority="high" decoding="async" class="alignnone wp-image-7441 size-medium" data-src="https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise-298x300.png" alt="digital image processing" width="298" height="300" data-srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise-298x300.png 298w, https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise-150x150.png 150w, https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise-100x100.png 100w, https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise.png 442w" data-sizes="(max-width: 298px) 100vw, 298px" /><noscript><img fetchpriority="high" decoding="async" class="alignnone wp-image-7441 size-medium" src="https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise-298x300.png" alt="digital image processing" width="298" height="300" srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise-298x300.png 298w, https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise-150x150.png 150w, https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise-100x100.png 100w, https://www.ssla.co.uk/wp-content/uploads/2020/08/Salt-and-pepper-noise.png 442w" sizes="(max-width: 298px) 100vw, 298px" /></noscript></p><h5 style="text-align: left;"><span style="color: #000000;">Gaussian noise</span></h5><p><span style="font-weight: 400;"><br />
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.<br />
</span></p><h5 style="text-align: left;"><span style="color: #000000;">Speckle noise</span></h5><p><span style="font-weight: 400;"><br />
An active radar <a href="https://www.ssla.co.uk/sensor/">sensor</a> 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.<br />
</span></p><h5 style="text-align: left;"><span style="color: #000000;">Motion blur</span></h5><p><span style="font-weight: 400;"><br />
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.<br />
</span></p><h3 style="text-align: left;"><span style="color: #000000;">Filters</span></h3><p><span style="font-weight: 400;"><br />
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<br />
</span></p><h5 style="text-align: left;"><span style="color: #000000;">Median filter</span></h5><p><span style="font-weight: 400;"><br />
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.<br />
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. </span></p><p> </p><h5 style="text-align: left;"><span style="color: #000000;">Laplacian filter</span></h5><p> </p><p>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.</p><h5 style="text-align: left;"><span style="color: #000000;">Gaussian filter</span></h5><p><span style="font-weight: 400;"><br />
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.<br />
</span></p><h5 style="text-align: left;"><span style="color: #000000;">Applications:</span></h5><p><span style="font-weight: 400;"><br />
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.<br />
In <a href="https://www.ssla.co.uk/brain-computer-interface/">computer vision</a> 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,</span></p><h5>digital signal processing</h5><p>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.</p><p>Read More about <a href="https://en.wikipedia.org/wiki/Digital_image_processing">Image Processing</a></p></div></div><div class="vc_btn3-container red-button vc_btn3-inline" >
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