A Comprehensive Guide to Digital Image Processing Using MATLAB, 2nd edition

Digital Image Processing Using MATLAB, 2nd edition

Digital image processing is a fascinating and rapidly evolving field that has many applications in science, engineering, medicine, arts, and entertainment. MATLAB is a powerful and versatile software tool that can help you perform various tasks related to digital image processing, such as manipulation, analysis, visualization, and generation of images. In this article, we will introduce the basics of digital image processing using MATLAB, as well as some advanced topics and applications that you can explore further.

Digital Image Processing Using MATLAB, 2nd edition

Introduction

What is digital image processing?

Digital image processing is the process of applying mathematical operations and algorithms to digital images, which are composed of pixels (picture elements) that have numerical values representing their color or intensity. Digital image processing can be used for various purposes, such as enhancing the quality of images, extracting useful information from images, compressing images for storage or transmission, creating new images from existing ones, or recognizing objects or patterns in images.

What is MATLAB?

MATLAB is a software environment that allows you to perform numerical computations, data analysis, visualization, programming, and simulation. MATLAB stands for MATrix LABoratory, because it is designed to work with matrices and arrays, which are convenient data structures for storing and manipulating images. MATLAB also has many built-in functions and toolboxes that provide specialized functionality for different domains, such as signal processing, statistics, optimization, machine learning, computer vision, and more.

Why use MATLAB for digital image processing?

MATLAB has many advantages for digital image processing, such as:

• It is easy to learn and use. You can write concise and readable code in MATLAB that can perform complex operations on images with just a few lines of code.

• It has a rich set of functions and toolboxes that cover most of the common and advanced techniques in digital image processing. You can also find many examples and tutorials online that demonstrate how to use these functions and toolboxes.

• It has a powerful graphical user interface (GUI) that allows you to interactively display, explore, and manipulate images. You can also create your own GUIs using MATLAB's graphical components.

• It has a high-performance engine that can handle large-scale data sets and computations efficiently. You can also leverage the power of parallel computing and GPU acceleration using MATLAB's parallel computing tools.

• It has a flexible and extensible architecture that allows you to integrate MATLAB with other languages and platforms. You can call functions from C/C++, Java, Python, or other languages within MATLAB, or vice versa. You can also deploy your MATLAB code as standalone applications or web services using MATLAB Compiler or MATLAB Web App Server.

Basic concepts and operations

Image representation and formats

An image can be represented as a matrix or an array of pixels, where each pixel has a value that indicates its color or intensity. Depending on the type of image, there are different ways to encode these values. For example:

• A grayscale image has only one channel (or dimension) that represents the brightness of each pixel. The values range from 0 (black) to 255 (white) for an 8-bit image.

• A color image has three channels (or dimensions) that represent the red, green, and blue components of each pixel. The values range from 0 to 255 for each channel for an 8-bit image. This is called the RGB color space.

• A binary image has only two possible values for each pixel: 0 (black) or 1 (white). This is useful for representing shapes, edges, or masks.

MATLAB can read and write images in various formats, such as JPEG, PNG, BMP, TIFF, GIF, etc. You can use the imread and imwrite functions to load and save images, respectively. For example:

img = imread('lena.jpg'); % read an image from a file imshow(img); % display the image on the screen imwrite(img, 'lena.png'); % save the image to a different format

Image acquisition and display

Image acquisition is the process of capturing images from a source, such as a camera, a scanner, a microscope, or a screen. MATLAB can interface with various devices and protocols that support image acquisition, such as USB, FireWire, GigE Vision, etc. You can use the Image Acquisition Toolbox to access and control these devices and capture images or video streams from them. For example:

vid = videoinput('winvideo', 1); % create a video input object preview(vid); % preview the video stream on the screen img = getsnapshot(vid); % capture a single image from the stream imshow(img); % display the image on the screen delete(vid); % delete the video input object

Image display is the process of showing images on a device, such as a monitor, a projector, a printer, or a web browser. MATLAB can display images on various devices and formats using different functions and tools. For example:

• The imshow function displays an image on the screen in a figure window. You can adjust the contrast, zoom, pan, or crop the image using the toolbar buttons or keyboard shortcuts.

• The imtool function displays an image on the screen in an interactive tool that allows you to inspect the pixel values, measure distances, adjust contrast, apply filters, or perform other operations on the image.

• The montage function displays multiple images as a single image in a grid layout. You can specify the number of rows and columns, the border size, and the background color of the montage.

• The imwrite function saves an image to a file in a specified format. You can also specify some parameters that affect the quality and size of the output file, such as compression ratio, bit depth, resolution, etc.

• The print function prints an image to a printer or saves it to a file in a vector or raster format. You can also specify some parameters that affect the appearance and layout of the output, such as paper size, orientation, margins, etc.

• The web function displays an image in a web browser. You can also specify some parameters that affect the behavior and interactivity of the browser, such as URL, target window, options, etc.

Image enhancement and restoration

Image enhancement is the process of improving the quality of an image by modifying its appearance or features. Image enhancement can be used for various purposes, such as increasing contrast, brightness, sharpness, or colorfulness of an image; removing noise or artifacts; highlighting edges or details; or applying artistic effects. MATLAB has many functions and toolboxes that provide different techniques for image enhancement. For example:

• The imadjust function adjusts the contrast or brightness of an image by mapping its intensity values to a new range. You can specify the input and output ranges manually or automatically.

• The histeq function performs histogram equalization on an image to enhance its contrast by redistributing its intensity values uniformly. You can also specify a reference histogram to match.

• The imsharpen function enhances the sharpness of an image by applying an unsharp masking filter. You can specify the amount and radius of sharpening.

• The imnoise function adds noise to an image to simulate various types of noise sources or degradation processes. You can specify the type and parameters of noise.

• The medfilt2 function performs median filtering on an image to remove salt-and-pepper noise or preserve edges. You can specify the size and shape of the filter window.

• The fspecial and imfilter functions create and apply various types of filters to an image to modify its frequency content or spatial features. You can specify the type and parameters of filters.

• The colfilt function performs column-wise filtering on an image to apply nonlinear operations on blocks of pixels. You can specify the size and shape of blocks and the function to apply.

Image segmentation and edge detection

Image segmentation is the process of dividing an image into meaningful regions or objects based on some criteria, such as color, texture, shape, or intensity. Image segmentation can be used for various purposes, such as object detection, recognition, or tracking; scene understanding; medical imaging; or image compression. MATLAB has many functions and toolboxes that provide different techniques for image segmentation. For example:

• The imbinarize function converts a grayscale image to a binary image based on a threshold value. You can specify the threshold value manually or automatically.

• The graythresh function computes an optimal threshold value for a grayscale image using Otsu's method. You can also specify the number of levels to quantize the image.

• The edge function detects edges in an image using various methods, such as Sobel, Canny, Prewitt, Roberts, or LoG. You can specify the parameters and direction of edge detection.

• The bwboundaries function traces the boundaries of objects in a binary image and returns their coordinates and labels. You can also specify the connectivity and hole-filling options.

• The regionprops function measures various properties of regions or objects in a binary or labeled image, such as area, perimeter, centroid, orientation, eccentricity, etc. You can also specify the properties to measure.

• The watershed function performs watershed segmentation on a grayscale or binary image to separate touching or overlapping objects based on their intensity gradients. You can also specify the markers or connectivity options.

• The activecontour function performs active contour segmentation on a grayscale or binary image to find the contours of objects that match a predefined shape or template. You can specify the initial contour, iterations, and method.

Image compression and coding

Image compression is the process of reducing the size of an image file by removing some information that is redundant or irrelevant. Image compression can be used for various purposes, such as saving storage space, transmitting images faster, or encrypting images. MATLAB has many functions and toolboxes that provide different techniques for image compression and coding. For example:

• The imwrite function can save an image to a file in a compressed format, such as JPEG, PNG, GIF, etc. You can also specify some parameters that affect the compression ratio and quality of the output file.

• The dct2 and idct2 functions perform discrete cosine transform (DCT) and inverse DCT on an image or a block of pixels to convert it from spatial domain to frequency domain or vice versa. DCT is widely used for lossy compression of images.

• The dwt2 and idwt2 functions perform discrete wavelet transform (DWT) and inverse DWT on an image or a block of pixels to decompose it into subbands of different frequency and spatial resolution. DWT is widely used for lossless compression of images.

• The huffmanenco and huffmandeco functions perform Huffman encoding and decoding on an image or a vector of symbols to compress it using variable-length codes based on their probabilities. Huffman coding is widely used for entropy coding of images.

• The arithenco and arithdeco functions perform arithmetic encoding and decoding on an image or a vector of symbols to compress it using fixed-length codes based on their probabilities. Arithmetic coding is widely used for entropy coding of images.

• The lloyds function performs Lloyd's algorithm to design an optimal quantizer for an image or a vector of symbols to reduce its bit rate by assigning them to discrete levels. Quantization is widely used for lossy compression of images.

Advanced topics and applications

Image analysis and feature extraction

Image analysis is the process of extracting useful information from images based on their content or features. Image analysis can be used for various purposes, such as object recognition, face detection, optical character recognition (OCR), biometrics, medical diagnosis, etc. MATLAB has many functions and toolboxes that provide different techniques for image analysis and feature extraction. For example:

• The imhist function computes and displays the histogram of an image or a region of interest (ROI) to show its intensity distribution. You can also specify the number of bins or levels.

• The improfile function extracts and plots the intensity profile of an image along a line segment or a polyline. You can also specify the interpolation method and number of points.

• The corr2 function computes the correlation coefficient between two images or regions of interest (ROI) to measure their similarity or alignment. You can also specify the normalization option.

• The normxcorr2 function computes the normalized cross-correlation between two images or regions of interest (ROI) to measure their similarity or alignment. You can also specify the normalization option.

• The imgradient function computes the gradient magnitude and direction of an image using various methods, such as Sobel, Prewitt, Roberts, or central difference. You can also specify the direction option.

• The imgradientxy function computes the gradient components along the x and y directions of an image using various methods, such as Sobel, Prewitt, Roberts, or central difference. You can also specify the direction option.

• The corner function detects corners in an image using various methods, such as Harris, Shi-Tomasi, or Minimum Eigenvalue. You can also specify the quality and sensitivity factors.

• The detectSURFFeatures function detects SURF (Speeded Up Robust Features) keypoints in an image that are invariant to scale, rotation, and illumination changes. You can also specify the metric threshold and ROI.

• The extractFeatures function extracts feature vectors or descriptors from an image based on the locations and scales of keypoints detected by various methods, such as SURF, BRISK, FREAK, etc. You can also specify the method and parameters.

• The matchFeatures function matches feature vectors or descriptors extracted from two images based on their similarity or distance. You can also specify the method, metric, and match threshold.

Image registration and fusion

Image registration is the process of aligning two or more images of the same scene or object taken from different viewpoints, times, sensors, or conditions. Image fusion is the process of combining two or more images of the same scene or object to produce a new image that has more information or better quality than any of the input images. MATLAB has many functions and toolboxes that provide different techniques for image registration and fusion. For example:

• The cpselect function allows you to interactively select control points in two images to establish correspondences between them. You can also use the cpcorr function to refine the control points automatically.

• The fitgeotrans function estimates a geometric transformation that maps the control points from one image to another. You can also specify the type of transformation, such as affine, projective, polynomial, etc.

• The imwarp function applies a geometric transformation to an image to warp it according to a specified mapping. You can also specify the interpolation method and output limits.

• The imregister function performs automatic image registration by estimating a geometric transformation that aligns two images based on their intensity values. You can also specify the type of transformation, optimizer, and metric.

• The imfuse function creates a composite image by blending two images using various methods, such as false color, difference, blend, etc. You can also specify the blending parameters and colormap.

• The vision.AlphaBlender System object performs alpha blending on two images by combining them with a weighted average of their pixel values. You can also specify the opacity and output data type.

• The vision.ImageCombiner System object performs image fusion on two images by combining them with a specified arithmetic operation, such as addition, subtraction, multiplication, etc. You can also specify the scaling factor and output data type.

Image recognition and classification

Image recognition is the process of identifying objects or patterns in images based on their features or descriptors. Image classification is the process of assigning labels or categories to images based on their content or features. MATLAB has many functions and toolboxes that provide different techniques for image recognition and classification. For example:

• The vision.CascadeObjectDetector System object detects objects in an image using a cascade of boosted classifiers trained with Haar-like features. You can also specify the object type, such as face, eye, nose, mouth, etc.

• The vision.PeopleDetector System object detects people in an image using a histogram of oriented gradients (HOG) features and a linear support vector machine (SVM) classifier. You can also specify the window size and classification threshold.

• The ocr function performs optical character recognition (OCR) on an image to extract text and its location. You can also specify the language and character set.

• The trainImageCategoryClassifier function trains an image category classifier using bag-of-features (BoF) representation and a linear support vector machine (SVM) classifier. You can also specify the feature extraction method and parameters.

• The classify function classifies an image into one of the categories learned by an image category classifier. You can also specify the confidence score output option.

the pretrained AlexNet deep convolutional neural network (CNN) that can recognize 1000 object categories. You can also modify or fine-tune the network for your own tasks.

• The activations function extracts the feature maps or activations of a layer in a deep convolutional neural network (CNN) for a given input image. You can also specify the output format and execution environment.

• The classify function classifies an image into one of the categories learned by a deep convolutional neural network (CNN). You can also specify the execution environment and top-k output option.

Image synthesis and generation

Image synthesis is the process of creating new images from existing ones or from scratch based on some rules or models. Image generation is the process of producing realistic images that match a given specification or condition. MATLAB has many functions and toolboxes that provide different techniques for image synthesis and generation. For example:

• The imresize function resizes an image by a specified scale factor or to a specified output size. You can also specify the interpolation method and antialiasing option.

• The imcrop function crops an image to a specified region of interest (ROI) or interactively. You can also specify the output size and position.

• The imtransform function applies a geometric transformation to an image to warp it according to a specified mapping. You can also specify the interpolation method and output limits.

The imtranslate function tr