{"id":170031,"date":"2023-09-04T20:05:24","date_gmt":"2023-09-04T19:05:24","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=170031"},"modified":"2023-10-30T19:48:47","modified_gmt":"2023-10-30T18:48:47","slug":"image-processing-fundamental-principles-and-practical-uses","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/image-processing-fundamental-principles-and-practical-uses","title":{"rendered":"Image Processing: fundamental principles and practical uses"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"170031\" class=\"elementor elementor-170031\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-45d941c e-flex e-con-boxed e-con\" data-id=\"45d941c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-3bd82f54 e-con-full e-flex e-con\" data-id=\"3bd82f54\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7bc0eb50 elementor-widget elementor-widget-text-editor\" data-id=\"7bc0eb50\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h2><strong>Images contain a wealth of important information. While they are easily discernible to our accustomed eyes, they pose a real challenge in data analysis. The collection of techniques used for this purpose is known as &#8220;image processing.&#8221; In this article, you will explore the classic algorithms, techniques, and tools for processing data in the form of images.<\/strong><\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-12d9c4d9 elementor-widget elementor-widget-heading\" data-id=\"12d9c4d9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">What is Image Processing ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5543e7e4 elementor-widget elementor-widget-text-editor\" data-id=\"5543e7e4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>As the name suggests, image processing involves working with images, which can encompass various techniques. The final outcome may take the form of <strong>another image,<\/strong> a variation of the original, or simply a parameter of the image. This result can then be utilized for in-depth analysis or decision-making.<\/p><p>It <strong>constitutes the central part of computer vision<\/strong>, playing a crucial role in numerous real-world applications such as robotics, autonomous vehicles, and object detection. Image processing enables us to transform and manipulate thousands of images simultaneously, extracting valuable information from them.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2c304894 elementor-widget elementor-widget-heading\" data-id=\"2c304894\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">But what is an image?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1fd84a16 elementor-widget elementor-widget-text-editor\" data-id=\"1fd84a16\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>An image can be represented as a 2D function, F(x, y), where x and y are spatial coordinates. Essentially, it&#8217;s a grid of pixels arranged in rows and columns. The value of F at a point x, y is known as the intensity of the image at that point. When x, y, and the amplitude value are finite, it&#8217;s referred to as a digital image.<\/p><p>An image can also be represented in 3D, with coordinates x, y, and z. Pixels are then arranged in the form of a matrix. This is what we call an RGB image, where R stands for red, G for green, and B for blue. In the case of grayscale images, there&#8217;s only one channel, and z equals 1.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-64c77885 elementor-widget elementor-widget-image\" data-id=\"64c77885\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"430\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Representation-Fond-Blanc@2x-1024x550.png\" class=\"attachment-large size-large wp-image-170034\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Representation-Fond-Blanc@2x-1024x550.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Representation-Fond-Blanc@2x-300x161.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Representation-Fond-Blanc@2x-768x412.png 768w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Representation-Fond-Blanc@2x-1536x824.png 1536w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Representation-Fond-Blanc@2x.png 1742w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Illustration d'une image RVB<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3874dd99 elementor-widget elementor-widget-heading\" data-id=\"3874dd99\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Classical image processing techniques <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4ef9432f elementor-widget elementor-widget-text-editor\" data-id=\"4ef9432f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Historically, images were processed using mathematical analysis methods, and here are a few of them:<\/p><ul><li><strong>Gaussian Blur:<\/strong> Gaussian blur, or Gaussian smoothing, is the result of applying a Gaussian function to an image, which is essentially a matrix as defined earlier. It is used to reduce image noise and soften details. The visual effect of this blurring technique is akin to looking at an image through a translucent screen. It is sometimes used as a data augmentation technique for deep learning, which we will discuss in more detail below.<\/li><li><strong>Fourier Transform:<\/strong> The Fourier transform decomposes an image into sinusoidal components. It has multiple applications, including image reconstruction, image compression, and image filtering. Since we&#8217;re discussing images, we&#8217;ll consider the discrete Fourier transform.<\/li><\/ul><p>Consider a<strong> sine wave<\/strong>; it comprises three elements:<\/p><ol><li>Amplitude &#8211; related to contrast<\/li><li>Spatial frequency &#8211; related to brightness<\/li><li>Phase &#8211; related to color information<\/li><\/ol><p>\u00a0<\/p><ul><li><strong>Edge Detection:<\/strong> Edge detection is an image processing technique used to find the boundaries of objects in images. It works by detecting discontinuities in brightness. More precisely, edges are defined as local maxima of the image gradient, meaning areas where there is a significant change in pixel values. The most common edge detection algorithm is the Sobel operator.<\/li><li>These <strong>historical mathematical analysis methods<\/strong> have laid the foundation for modern image processing and computer vision techniques, enabling a wide range of applications in fields like image recognition, medical imaging, and more.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1b00822 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"1b00822\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Machine Learning Training<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-70c366a elementor-widget elementor-widget-heading\" data-id=\"70c366a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Image processing with neural networks <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-53da0d86 elementor-widget elementor-widget-image\" data-id=\"53da0d86\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"412\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Detection-des-contours@2x-1024x527.png\" class=\"attachment-large size-large wp-image-170035\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Detection-des-contours@2x-1024x527.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Detection-des-contours@2x-300x154.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Detection-des-contours@2x-768x395.png 768w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Image-Processing-Detection-des-contours@2x.png 1464w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Edge detection on an image of the Eiffel Tower<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-546c6e74 elementor-widget elementor-widget-text-editor\" data-id=\"546c6e74\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The emergence of neural networks and increased computational power has revolutionized image processing. In particular, convolutional<a href=\"https:\/\/datascientest.com\/en\/deep-neural-network-what-is-it-and-how-is-it-working\"> neural networks (CNNs)<\/a>, inspired by the previously mentioned techniques, excel in object and person detection and recognition. How do they work?<\/p><p>A Convolutional Neural Network, or CNN for short, consists of three main types of layers:<\/p><p><strong>1. Convolutional Layer (CONV):<\/strong> This layer is responsible for performing the convolution operation. The element involved in the convolution operation is called the kernel\/filter (matrix). The kernel performs horizontal and vertical shifts until the entire image is traversed. Its operation is similar to edge detection techniques.<\/p><p><strong>2. Pooling Layer (POOL):<\/strong> This layer is responsible for reducing dimensionality. It helps decrease the computational power required for data processing. There are two types of pooling: Max pooling and <strong>Average pooling.<\/strong> Max pooling returns the maximum value from the area covered by the kernel in the image, while average pooling returns the average of all values in the part of the image covered by the kernel.<\/p><p><strong>3. Fully Connected Layer (FC):<\/strong> The fully connected layer is present at the end of CNN architectures. It&#8217;s similar to a traditional layer in neural networks and, after applying an activation function, produces the network&#8217;s expected output, such as classification.<\/p><p>The addition of non-linear functions (e.g., ReLU &#8211; Rectified Linear Unit) within networks or specific architectures allows CNNs to address more complex problems. There are many examples of CNN architectures, including DenseNet,<a href=\"https:\/\/datascientest.com\/en\/u-net-computer-visions-neural-network\"> U-Net,<\/a> and VGG, each tailored to specific tasks and challenges in image processing. These architectures, along with techniques like transfer learning, have significantly improved the accuracy and efficiency of image recognition and analysis.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59ca9a06 elementor-widget elementor-widget-heading\" data-id=\"59ca9a06\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Generative Adversarial Networks<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66ce0232 elementor-widget elementor-widget-text-editor\" data-id=\"66ce0232\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400; color: #000000;\">Labelled data is sometimes too scarce for training complex networks. Generative Adversarial Networks, known as <strong>GANs, are particularly popular for this purpose today.<\/strong><\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-287f94e3 elementor-widget elementor-widget-text-editor\" data-id=\"287f94e3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>GANs<\/strong>, or Generative Adversarial Networks, consist of two models: the Generator and the Discriminator. The Generator learns to create fake images that appear realistic to deceive the Discriminator, while the Discriminator learns to distinguish fake images from real ones.<\/p><p>In the initial phase, the Generator is not allowed to see real images, which can lead to subpar results. Meanwhile, the Discriminator is exposed to real images mixed with fake ones produced by the Generator and must classify them as real or fake.<\/p><p>To introduce diversity, some noise is added to the Generator, ensuring it generates different examples each time rather than the same type of image repeatedly. Based on the scores predicted by the Discriminator, the Generator strives to improve its results. Over time, the Generator becomes capable of producing images that are increasingly difficult to distinguish, and the Discriminator also improves as it receives more realistic images from the Generator with each iteration.<\/p><p>Popular types of <strong>GANs include Deep Convolutional GANs (DCGAN), Conditional GANs (cGAN), StyleGAN, CycleGAN, and more.<\/strong><\/p><p>GANs are exceptionally well-suited for image generation and manipulation tasks such as aging faces, blending photos, super-resolution, photo painting, and clothing translation.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-717bacd9 elementor-widget elementor-widget-heading\" data-id=\"717bacd9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4438b4b3 elementor-widget elementor-widget-text-editor\" data-id=\"4438b4b3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>There are numerous image processing techniques available today, and neural networks play a pivotal role in extracting detailed information and achieving precise conclusions. Python is the preferred language for such tasks, and it offers a range of tools such as OpenCV, Scikit-Image, TensorFlow, and <a href=\"https:\/\/datascientest.com\/en\/pytorch-all-about-this-framework\">PyTorch.<\/a><\/p><p>The training programs offered by DataScientest provide a wealth of tools and resources to learn how to process image data and harness the power of even the most complex neural networks.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c4e46fb e-flex e-con-boxed e-con\" data-id=\"c4e46fb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-6af3465 e-con-full e-flex e-con\" data-id=\"6af3465\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c7bd8e0 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"c7bd8e0\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Images contain a wealth of important information. While they are easily discernible to our accustomed eyes, they pose a real challenge in data analysis. The collection of techniques used for this purpose is known as &#8220;image processing.&#8221; In this article, you will explore the classic algorithms, techniques, and tools for processing data in the form [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":170032,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Image Processing: fundamental principles and practical uses<\/title>\n<meta name=\"description\" content=\"Image processing is a fascinating field that involves the manipulation and analysis of digital images. It encompasses fundamental principles\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/image-processing-fundamental-principles-and-practical-uses\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Image Processing: fundamental principles and practical uses\" \/>\n<meta property=\"og:description\" content=\"Images contain a wealth of important information. 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