Digital Image Processing Tutorial
Last Updated :
12 Apr, 2024
In this tutorial, we will learn all about Digital Image Processing or DIP which is a subcategory of signal processing that particularly deals with the manipulation of digital images by using a digital computer. It is based on the principle of the I-P-O cycle, where it will take a digital image as an input and process it to get an image as an output.
This free DIP tutorial is designed from basics to advanced level so anyone, from beginners to professionals can easily get all the details of digital image processing. This tutorial includes all the modules like basics, MATLAB GUI, Image conversion, image filtering techniques, histogram equalization, object identification, edge detection in MATLAB, Image extensions, MATLAB built-in functions, array functions, etc.
What is Digital Image Processing (DIP)?
Digital Image Processing or DIP is software that takes a digital image as an input to process it to get an image as an output. In other words, DIP deals with the manipulation of digital images by using a digital computer. It is a subcategory of signals and systems where it can be easily understood by beginners if they know the basics of digital electronics.
It also requires the knowledge of basic calculus, probability, and differential equations for mathematical calculation purposes. Also, there is a basic knowledge of programming like MATLAB, C++, etc.
Digital Image Processing is used as a software for image processing. Photography is the mechanism that processes the image accordingly. Other examples are computer graphics, pixels, etc.
Examples of Digital Image Processing
The most common and highly used example is Adobe photoshop. As we discussed above, other examples are computer graphics, photography, camera, X Ray images, CT Scans, etc.
Working of Digital Image Processing
The working principle of Digital Image Processing is based on I-P-O cycle where takes input, process and then release output. Here, digital image acts as an input and after processing, it will become the desired image we want acts as an output. Lets take an example to elaborate the working of Digital Image Processing.
If we take a snap from a camera or mobile, and send that snap to digital system so that it can remove all the irrelevant part of that image and can just enhance the part we want. Like, a snap contains a view of a village and we want to just focus on 3rd cottage, then digital system will just focuses on that only by zooming it and the quality will not get compromised.
Introduction to DIP (Digital Image Processing)
Digital Image Processing (DIP) is a field of study and practice on manipulating and analyzing of digital images using digital Algorithms. It deals with the images which are represented in the digital format, basically array of pixels. In DIP, the images are processed to enhance their quality or to perform various operation such as segmentation, restoration and recognition. It has various Applications in domains such as medicine, remote sensing, surveillance and more.
For Example, DIP Techniques are used to enhance clarity of X-ray images which provides healthcare professionals to detect abnormalities more accurately.
DIP Basics
What is Image Processing ?
What is an Image in DIP?
What is Digital Image Processing ?
Fundamental Steps in Digital Image Processing
Components of Digital Image Processing
Applications of Digital Image Processing
Analog Image Processing
History of Photography
Concept of Dimensions in DIP
Image Formation on Camera
Camera Mechanism
What is Pixel?
Concept of Bits Per Pixel
Types of Images
Binary Image
Concept of Sampling
Pixel Resolution
Image Conversion
Image conversion is the process of converting images from one format to another format. The image conversion is important in digital image processing as different devices and software supports different image formats. Image conversion can be performed using different tools and services. Overall image conversion is an important and valuable process that allows user to change image format for different platforms, devices, and usage scenarios, ensuring compatibility, efficiency, and optimal visual quality.
Introduction to Digital Images
Color Codes Conversion
RGB image representation
How to convert RGB image to binary image
YIQ Color Model
How to convert YIQ image to RGB image
How to convert RGB image to YIQ image
What is Grayscale image?
What are the advantages and disadvantages of HSV color ?
How to convert RGB image to grayscale image without using rgb2gray function
What is Otsu Thresholding ?
Otsu's thresholding without graythresh function
What is HSI Color Space ?
Converting RGB Image to HSI Image
Converting HSI Image to RGB Image
How to partially colored gray image in MATLAB
What is the HSV (Hue, Saturation, Value) Color Model?
What are the advantages and disadvantages of HSV color ?
Color Slicing using HSV color space
Image Filtering Techniques
Image Filtering Techniques are one of the important tools that are used in the digital image processing which provides different methods to improve, modify or extract information from the images. One of the the primary functions of the image processing is to reduce the unwanted noise that can degrade the image quality, to reduce this ,image filtering techniques are used to smooth out irregularities and improve the clarity of image. Overall image filter are important in the digital image processing to perform various functions such as reducing noise, detecting edges, enhancing details, or manipulating colors.
What are Image Filtering Techniques ?
Spatial Filtering and its Types
Frequency Domain Filters and its Types
What is Median filtering ?
Median filtering for salt and pepper noise without using medfilt2 function
What is moving average filter ?
How to decide window size for a moving average filter?
Noise Models in Digital Image Processing
Gaussian Noise
Rayleigh Noise
Erlang (or gamma) Noise
Exponential Noise
Uniform Noise
Impulse NoiseÂ
How to apply Median filter for RGB image in MATLAB
What is moving average filter ?
Linear filtering without using imfilter function
Add 'Salt and pepper'or 'Speckle' noise to an image
What is Linear filtering ?
What is Adaptive filtering- Local Noise filter
One Dimensional Low pass filtering
High Pass filtering
Band pass filtering
MATLAB - Converting a Grayscale Image to Binary Image using Thresholding
Gaussian Filtering/ Gaussian Blur
Upsampling in MATLAB
Upsampling in Frequency Domain
Convolution Shape (full/same/valid) in MATLAB
Linear Convolution using C and MATLAB
Circular Convolution using C and MATLAB
Histogram Equalization
Histogram Equalization is one of the fundamental technique used in image processing to improve contrast and brightness of the image. This method works by adjusting the Pixel density distribution of an image to create more uniform histograms which results in improving the visual quality and clarity. Histogram equalization is used in the various image processing such as medical imaging, satellite imaging, digital photography, and computer vision.
Histogram Equalization in Digital Image Processing
Histogram equalization without using histeq() function.[Global]
Local Histogram equalization in MATLAB
Color Histogram equalization
Identifying objects based on label(bwlabel)
Object Identification and Edge Detection
Object Identification and Edge Detection are techniques of digital image processing where we can find out the boundaries or area covered by objects within an image and it can be done by using image segmentation and data extraction. It is useful and implemented in many areas like computer and machine vision, image processing, medical imaging, remote sensing, pattern recognition. It can be implemented by using 4 important steps named as smoothing, enhancement, detection and localization.
MATLAB output function
Cartesian to Polar co-ordinates
Identifying objects based on label(bwlabel)
Image shading in MATLAB
Edge detection using in-built function in MATLAB
Digital Image Processing Algorithms using MATLAB
Sobel edge detection
Edge Detection -Fundamentals
Image Sharpening using second order derivative - (Laplacian)
Edge detection using Local Variance
PhotoShop Effects in MATLAB
In MATLAB, users can replicate various photoshop effects by using various image processing techniques. By using these techniques user can make similar photoshop visual effects like Adobe Photoshops. For this process, MATLAB is used as it is flexible and it has extensive toolbox which makes it a powerful platform for implementing a wide range of image effects and enhancements. These includes effects like motion blur, sharpening techniques, color adjustments, denoising algorithms, artistic effects such as oil painting or watercolor, geometric transformations, text overlays, and more.
What is Swirl Effect in MATLAB
Oil Painting in MATLAB
Cone effect in MATLAB
Glassy effect in MATLAB
Tiling effect in MATLAB
These techniques are the integral components of the digital image processing. The image Geometry is used to deal with the spatial arrangements in the digital images, it includes techniques for image adjustment, such as resizing, cropping, and rotating. The Optical illusion are used for visual Phenomena, it contains various techniques to create or replicate optical illusions. The Image Transformation are the broad range of techniques used for altering the appearance or content of digital images. These Techniques has various applications in photography, graphic design, medical imaging, and scientific research.
Geometric Transformation in Image Processing
Image rotation by Matlab without using imrotate
Image Resizing in Matlab
Matlab program to rotate an image 180 degrees clockwise without using function
Optical Illusion in Digital Image Processing
Nearest-neighbor interpolation algorithm in MATLAB
Black and white optical illusion
MATLAB | Complement colors in a Binary image
2-D Discrete Cosine Transform
2-D Inverse Cosine Transform
Intensity Transformation Operations on Images
What is Fast Fourier Transform?
Fast Fourier Transform on 2 Dimensional matrix using MATLAB
Gray Level Transformation
Gray Scale to Pseudo Color Transformation
What is Image Enhancement?
Linear Contrast Enhancement
Balance Contrast Enhancement Technique
Morphological Image Processing, Compression and Files
These Processing techniques are the important components of digital image processing. The Morphological Image Processing involves the analysis and manipulation of image shape and structure using mathematical techniques which includes function such as dilation, erosion, opening, and closing, that are used to enhance, segment, or extract features from images based on their geometric properties.
The Compression is the techniques used to reduce the size of the digital image file while preserving its quality. This is done by eliminating redundant or irrelevant information from images. The File handling technique is the process of managing, manipulating and storing the image files in the different formats. These techniques has wide range of applications in science, engineering, medicine, and multimedia.
What is morphological image processing?
What are data compression techniques?
Boundary Extraction
MATLAB: Connected Component labeling without using bwlabel or bwconncomp functions
Morphological Operations in MATLAB
MATLAB Erosion of an Image
Auto Cropping- Based on labeling the connected components
Run Length Encoding & Decoding in MATLAB
Lossless Predictive Coding
Bit Plane Compression
How to read text file backwards?
Read words in a file in reverse order
How to read image file or complex image file in MATLAB
Image Transformation is the process of changing or alerting the appearance or characteristics of digital images through various mathematical operations. These operations involves different techniques to modify the spatial properties of images such as size, orientation, and position to get the desired visual effect or to extract the specific information. Image Transformation is widely used in the DIP for performing operation such as geometric correction, image registration, and feature extraction.
What is Image Coding ?
What is image comparison?
What is a texture in image processing?
2-D Discrete Cosine Transform
2-D Inverse Cosine Transform
Intensity Transformation Operations on Images
Fast Fourier Transform on 2 Dimensional matrix using MATLAB
Gray Scale to Pseudo Color Transformation
Linear Contrast Enhancement
Balance Contrast Enhancement Technique
Image Coding, Comparison and Texture Features
Image Coding ,Comparison and Text features are the fundamental aspects in the DIP. Image coding is the techniques for efficiently representing and compressing the digital images. It contains different compression algorithms such as JPEG, PNG, and GIF. Comparison techniques are used for assessing similarities or differences between images. It includes Methods like computing the similarity metrics, such as correlation coefficients or structural similarity indices, to find the degree of resemblance between images.
The Texture feature is the Spatial arrangement of pixel intensities in an image that provides the information about surface properties such as smoothness, roughness, or granularity. These Techniques plays important role in advancing the field of digital image processing.
Digital Image Watermarking
Hide the message or image inside an image
How do I match a template in Matlab?
Grey Level Co-occurrence Matrix (GLCM) in MATLAB
Texture Measures from GLCM – MATLAB CODE
Difference Between
This part of the tutorial contains important differences or comparison based topics based on digital image processing where you can see differences of types of images, color models, types of noise etc. All the differences articles will give you an structured tabular format differences for comparison with clarity.
Analog Image Processing vs. Digital Image Processing
Difference Between RGB, CMYK, HSV, and YIQ Color Models
Difference between dilation and Erosion
Differentiate between grayscale and RGB images
Monochrome vs. Grayscale
Difference between binary and grayscale images?
Differences between RGB and CMYK color schemes
Portable Cameras vs. Digital Cameras
Difference between 8-bit and 16 bit color format
Difference between salt noise and pepper noise
Conclusion
With a clear focus on practical techniques and real-world applications, this digital image processing tutorial equips learners with the essential skills to navigate the complexities of digital image processing seamlessly on the areas MATLAB GUI, Image conversion, image filtering techniques, histogram equalization, object identification, edge detection in MATLAB, Image extensions, MATLAB built-in functions, array functions, etc. Whether you're a beginner or an experienced, embracing the knowledge will undoubtedly elevate your proficiency in digital image processing to new heights. Explore, learn, and unlock the boundless possibilities of digital imagery with this definitive tutorial.
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MATLAB: Connected Component Labeling without Using bwlabel or bwconncomp Functions
A connected component or object in a binary image is a set of adjacent pixels. Determining which pixels are adjacent depends on how pixel connectivity is defined. There are two standard classification connections for 2D images. Connected-4 - Pixels are connected when the edges touch. Two adjacent pi
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Morphological operations in MATLAB
Morphological Operations is a broad set of image processing operations that process digital images based on their shapes. In a morphological operation, each image pixel is corresponding to the value of other pixel in its neighborhood. By choosing the shape and size of the neighborhood pixel, you can
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Matlab | Erosion of an Image
Morphology is known as the broad set of image processing operations that process images based on shapes. It is also known as a tool used for extracting image components that are useful in the representation and description of region shape. The basic morphological operations are: Erosion DilationIn t
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Auto Cropping- Based on Labeling the Connected Components using MATLAB
Auto Cropping Based on labeling the connected components is related to digital image processing. So, In this article, we will discuss the Auto Cropping-Based on labeling the connected components. Before that look at the basic terms which are required in this topic. Auto CroppingIn terms of MATLAB, t
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Run Length Encoding & Decoding in MATLAB
Run-length encoding, or RLE, is a straightforward method of lossless data compression in which runs of data, or sequences of data with the same value in many consecutive elements, are stored as a single value and count rather than as the original run. Â In other words, RLE ( Run Length coding) is a s
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Lossless Predictive Coding in MATLAB
In Lossless Predictive Coding A new pixel value is obtained by finding the difference between the predicted pixel value and the current pixel. In other words, this is The new information of a pixel is defined as the difference between the actual and predicted value of that pixel.  The approach comm
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Extract bit planes from an Image in Matlab
Image is basically combination of individual pixel (dots) information. When we write that image is of 620 X 480 size, it means that image has 620 pixel in horizontal direction and 480 pixel in vertical direction. So, altogether there is 620 X 480 pixels and each pixels contains some information abou
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How to Read Text File Backwards Using MATLAB?
Prerequisites: Write Data to Text Files in MATLAB Sometimes for some specific use case, it is required for us to read the file backward. i.e. The file should be read from EOF (End of file Marker) to the beginning of the file in reverse order. In this article we would learn how to read a file in back
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MATLAB - Read Words in a File in Reverse Order
MATLAB stands for Matrix Laboratory. It is a high-performance language that is used for technical computing. It allows matrix manipulations, plotting of functions, implementation of algorithms, and creation of user interfaces Suppose we are given a text file containing the following words "I STUDY F
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How to Read Image File or Complex Image File in MATLAB?
MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models. For Image Reading in MATLAB, we use the image processing toolbox. In this ToolBox, there are many methods such as imread(), imshow() etc. imshow
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Image Coding, Comparison and Texture Features
Digital Watermarking and its Types
Digital Watermarking is use of a kind of marker covertly embedded in a digital media such as audio, video or image which enables us to know the source or owner of the copyright. This technique is used for tracing copyright infringement in social media and knowing the genuineness of the notes in the
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How To Hide Message or Image Inside An Image In MATLAB?
MATLAB is a high-performance language that is used for matrix manipulation, performing technical computations, graph plottings, etc. It stands for Matrix Laboratory. With the help of this software, we can also hide a message or image inside an image. Following are the steps to hide an image, which c
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How to Match a Template in MATLAB?
Moving the template across the entire image and comparing it to the image's covered window is a process known as "template matching." The implementation of template matching involves two-dimensional convolution. Template matching has different applications and is utilized in such fields as face ackn
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Grey Level Co-occurrence Matrix in MATLAB
The use of texture to identify regions of interest in an image is a crucial characteristic. One of Haralick et al.'s earliest approaches to texture feature extraction was Grey Level Co-occurrence Matrices (GLCM) Â in the year 1973. Since then, it has been used extensively in a number of texture analy
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MATLAB - Texture Measures from GLCM
GLCM stands for Gray Level Co-occurrence Matrix. In image processing, The GLCM function computes how often pairs of pixels with a particular value and in a particular spatial relationship occur in an image, constructs a GLCM, and extracts statistical measures from this matrix to determine the textur
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