Image Processing Lecture Notes PDF

# IMAGE PROCESSING ## by DR Reham Amin - [email protected] - Author: Dr. Reham Amin # What do you expect to learn from this course? # Outline - Course Textbook - Course Links - Course Contents - What is an image? - Types of images - What is a digital image? - How to obtain a digit...

# IMAGE PROCESSING ## by DR Reham Amin - [email protected] - Author: Dr. Reham Amin # What do you expect to learn from this course? # Outline - Course Textbook - Course Links - Course Contents - What is an image? - Types of images - What is a digital image? - How to obtain a digital image? - What is image processing? - Phases of Image Processing # Course Textbook - Digital Image Processing Using MATLAB, 3rd edition - Rafael Gonzalez, Richard Woods - Publication year: 2017 - Print-ISBN: 978-1-292-22304-9 - E-ISBN: 978-1-292-22307-0 - Pages: 1024 - Author: Dr. Reham Amin # What is an image? - An image is a two-dimensional function that represents a measure of some characteristic such as brightness or color of a viewed scene. - An image is a projection of a 3-D scene into a 2D projection plane. - An image may be defined as a two-dimensional function $f(m,n)$, where: - $m$ and $n$ are spatial (plane) coordinates (row and column values). - the amplitude of $f$ at any pair of coordinates $(m,n)$ is called the **intensity** of the image at that point. - Pixel values are gray levels in range 0-255 or RGB colors. # Types of images - Binary image - Grayscale (8 bit) image - RGB Color image - RGBA Image # Binary Image # Grayscale (8-bit) images - An image of the Golden Gate Bridge is shown, before and after being converted to grayscale. - Both images are accompanied by a histogram of their pixel intensity values. # The effect of different number of intensity levels - An image of a woman is shown, on the left with 256 intensity levels and on the right with 32 intensity levels. - The number of intensity levels on the right side is progressively reduced to 16, 8, and 2. - It can be seen that as the number of intensity levels decreases, the image becomes more and more like a cartoon. # RGB Color image - Color images are formed by a combination of individual 2-D images. - For example: The RGB color system, a color image consists of three (red, green and blue) individual component images. - For this reason many of the techniques developed for monochrome images can be extended to color images by processing the three component images individually. - These color values are represented as different channels. Like the gray-scale image, each channel is an image. - In addition to accessing each image intensity with a row and column index, we also have an **index for each channel**, in this case, 0 for red, 1 for blue and 2 for green. - An image may be continuous with respect to the x- and y- coordinates and also in amplitude. Converting such an image to digital form requires that the coordinates, as well as the amplitude, be digitized. # RGB Color Image - It is represented by a **16-bit matrices** to computers. - Three equal-sized matrices (called **channels**), each having values ranging from 0 to 255. - Channels are stacked on top of each other, and thus we require three unique coordinates to specify the value of a matrix element. - Thus, a pixel in an RGB image will be of color: - black when the pixel value is (0, 0, 0) - white when it is (255, 255, 255) - Red when it is (255, 0, 0) - Green when it is (0, 255, 0) - Blue when it is (0, 0, 255) - A diagram of a monkey is shown, where each pixel is represented by a three-dimensional vector. # RGBA Image - RGBA images are colored RGB images with an extra channel known as "alpha" that depicts the opacity of the RGB image. - Opacity ranges from a value of 0% to 100% and is essentially a "see-through" property. - Opacity in physics depicts the amount of light that passes through an object. - For instance, cellophane paper is transparent (100% opacity), frosted glass is translucent, and wood is opaque. - The alpha channel in RGBA images tries to mimic this property. <start_of_image> # So what is a DIGITAL IMAGE??? # WHAT IS A DIGITAL Image? - Digital image is a **finite collection** of discrete pixels of any observable object. - The pixels represent a two- or higher dimensional "view" of the object, each pixel having its own discrete value in a finite range. - The pixel values may represent the amount of visible light, infra red light, absortation of x-rays, electrons, or any other measurable value such as ultrasound wave impulses. - The images may be obtained by a digital camera, scanner, electron microscope, ultrasound stethoscope, or any other optical or non-optical sensor. - Examples of digital image are: - Digital photographs - Satellite images - Radiological images (x-rays, mammograms) - Binary images - Fax images - Engineering drawings # How to obtain a digital image? - A diagram is shown, depicting how a scene is captured by a camera, converted into a digital image, and displayed. # What is image processing? - The field of digital image processing refers to processing digital images by means of a **digital computer**. - The input is an image, and the output can be a better image or some important details from the image. - Image processing involves performing operations on an image to make it better or to get important information from it. It's like fixing or improving a picture, and it's a bit like working with signals. - Digital image processing encompasses processes whose inputs and outputs are images and, in addition, includes processes that extract attributes from images up to, and including, the recognition of individual objects. - For example, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. # Outputs of these processes generally are images - A diagram is shown, depicting the different phases of image processing. - These phases include: - Image acquisition - Image filtering and enhancement - Image restoration - Color image processing - Wavelets and other image transforms - Compression and watermarking - Morphological processing - Segmentation - Feature extraction - Image pattern classification # Phases of Image Processing - A diagram is shown, depicting the different steps of image processing. These steps include: - Image Acquisition - Image Enhancement - Image Restoration - Color Image Processing - Wavelets and Multiresolution Processing - Compression - Morphological Processing - Segmentation - Representation and Description - Object Recognition # Image Acquisition - The image is **captured by a camera and digitized** (if the camera output is not digitized automatically) using an **analogue-to-digital converter** for further processing in a computer. - A diagram of a camera system is shown, showing the different components involved in image acquisition. # Image Enhancement - In this step, the acquired image is manipulated to meet the requirements of the specific task for which the image will be used. - Such techniques are primarily aimed at **highlighting the hidden or important details in an image**, like **contrast and brightness adjustment**, etc. Image enhancement is highly subjective in nature. - An image is shown, before and after enhancement. # Image Restoration - This step deals with **improving the appearance** of an image and is an objective operation since the degradation of an image can be attributed to a mathematical or probabilistic model. For example, **removing noise or blur from images**. - An image is shown, before and after restoration. # Color Image Processing - This step aims at handling the processing of colored images (16-bit RGB or RGBA images), for example, performing **color correction or color modeling in images**. # Wavelets and Multi-Resolution Processing - Wavelets are the **building blocks for representing images in various degrees of resolution**. Images subdivision successively into smaller regions for **data compression and for pyramidal representation**. - A diagram is shown, depicting how an image can be represented using wavelets. # Image Compression - **COMPRESSION DONE PROPERLY: INVISIBLE TO THE NAKED-EYE** - An image is shown, before and after compression. # Morphological Processing - Image components that are **useful in the representation and description of shape need to be extracted for further processing or downstream tasks**. Morphological Processing provides the tools (which are essentially **mathematical operations**) to accomplish this. For example, erosion and dilation operations are used to **sharpen and blur the edges of objects in an image**, respectively. - A diagram is shown, demonstrating the different stages of morphological processing. # Image Segmentation - This step involves **partitioning an image into different key parts to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze**. Image segmentation allows for computers to put **attention on the more important parts of the image**, discarding the rest, which enables automated systems to have improved performance. - A diagram is shown, illustrating the purpose of image segmentation. # Object Detection and Recognition - After the objects are **segmented from an image** and the representation and description phases are complete, the automated system needs to **assign a label to the object** -to let the human users know what object has been detected, for example, "vehicle" or "person", etc.. - Two images are shown, one where the objects are recognized and another where they are not. # Knowledge Base - Knowledge may be as simple as the bounding box coordinates for an object of interest that has been found in the image, **along with the object label** assigned to it. Anything that will help in solving the problem for the specific task at hand can be encoded into the knowledge base. # Thank you