Best Digital Image Processing Books You Must Read

Posted on 11-09-2017 by onlinebooksreview

Digital image processing is the use of computer algorithms to perform image processing on digital images. It has important application in satellite imagery, wire-photo standard conversion, medical imaging, videophone, character recognition and photograph enhancement.

If you're looking for book recommendation on digital image processing, then you've come to the right place. Today in this article I've listed some best books on this topic. These books can be helpful for students, researchers, professionals, and anyone working in this field. So, hurry up and pick up the books!

Here you will get some best books on Image processing.

Digital Image Processing (4th Edition)

This book has been one of the most popular books on image processing for 40 years. This is an ideal book for students from any technical subject taking digital image processing course. It mainly focuses on the fundamentals of this field. In this updated edition, you'll find detailed explanations of extended topics such as deep learning and deep neural networks. It also offers a more cohesive presentation in reorganizing the material on image transforms. With this guide, you'll learn material that is fundamental but has a broad scope of application.

What You Will Learn:

  • Image fundamentals
  • Image enhancement in the spatial and frequency domains
  • Color image processing
  • Image restoration
  • Image compression
  • Applying spatial kernels and spatial filtering to images
  • Fundamentals of object recognition
  • The scale-invariant feature transform (SIFT)
  • Finding maximally-stable extremal regions (MSERs)
  • Working with k-means clustering and superpixels
  • Exact histogram matching

The Digital Negative: Raw Image Processing in Lightroom, Camera Raw, and Photoshop (2nd Edition)

This is a useful book for digital photographers. It presents hands-on and effective techniques for exposing and shooting for raw image capture and developing a raw processing workflow. With this guide, you'll understand how to extract the best-possible raw rendering of your digital negatives and using Photoshop to achieve the highest quality of your images. You'll also know a foolproof process for working with your digital negatives and optimizing raw images.

What you'll learn:

  • Creating an efficient workflow
  • Fundamentals of Lightroom and Camera Raw
  • Extracting the best tone and color from your digital negatives
  • Using Lightroom and Camera Raw sharpening controls to maximize image quality
  • Processings for panoramic and HDR images
  • Producing stunning black and white images
  • Using Smart Objects and Layer Blending in photoshop
  • Working with HDR in Camera Raw and Lightroom

Feature Extraction and Image Processing for Computer Vision, Third Edition

This is an essential guide for those who've been working with image processing and computer vision. It presents the most important algorithms used in image processing and computer vision in a comprehensive manner. With this guide, you'll get a complete understanding of the methods and techniques to work with the digital image. It is an ideal book for engineers and students working in this cutting-edge field.

What you'll learn:

  • Basic image processing operations
  • low-level feature extraction
  • Edge detection in your image
  • High-level feature extraction including fixed shape matching and deformable shape analysis
  • Working with texture description, segmentation, and classification
  • Working with moving object detection and description

Image Processing: The Fundamentals

Image Processing: The Fundamentals is the ideal text for anyone seeking a book to understand the essential concepts of image processing. This thoroughly updated combines the classics topics in image processing like orthogonal transforms and image enhancement as well as modern concepts like image processing and color, sine and cosine transform, Independent Component Analysis (ICA), phase congruency and the monogenic signal and several other new topics. Its main focuses on the understanding of how image processing methods work in practice. With this guide, you'll get the fundamental concepts and a broad view of image processing.

What you'll learn:

  • Basics of image transformation
  • Understanding singular value decomposition
  • Haar, Walsh, and Handmaid transform
  • Using discrete Fourier transformation in image processing
  • Both the even and odd symmetric discrete cosine transformation
  • Independent component analysis
  • Reducing high-frequency noise from your image
  • Reducing low-frequency interference
  • Histogram manipulation
  • Image restoration methods
  • Image segmentation and edge detection
  • Image processing techniques for multispectral images

Principles of Digital Image Processing: Core Algorithms (Undergraduate Topics in Computer Science)

This is a comprehensive book for learning digital image processing. Using simple and concise examples it explains the core principles of image processing. It is specially designed for undergraduate students who're taking fundamental courses on digital image processing. For this reason, this guide mainly focuses on the basics of image processing. But any professional can be benefited from this book by reviewing the fundamental techniques.

What you'll learn:

  • Finding and labeling regions in binary images
  • Core algorithms for image processing
  • Detecting simple curves and corners
  • Working with the color of your digital image
  • Introduction to spectral techniques including Fourier transform and discrete Cosine transform
  • Performing geometric operations on your images
  • Comparing images

Image Processing and Acquisition using Python (Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series)

Image Processing and Acquisition using Python is one of the major books where both image acquisition and image processing are explained. It provides a sound foundation in both of these topics. By improving your understanding of image acquisition techniques and corresponding image processing, this book will help you to perform experiments more effectively and cost efficiently. You'll also learn how to analyze and measure more accurately. With this book, you'll be able to read and write images using Python, and the basics of image processing. It's suitable for both refresher and experienced reader.

What you'll learn:

  • Basics of Python including statements, data types, and data structures
  • Computing using Python libraries such as Numpy, Scipy, Matpotlib, Python Imaging library and Scikits
  • Fundamental properties of image
  • Using spatial filters
  • Image enhancement
  • Fast Fourier transform using Python
  • Image segmentation including histogram-based segmentation and region based segmentation 
  • Morphological operations
  • Image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy

Principles of Digital Image Processing: Fundamental Techniques (Undergraduate Topics in Computer Science)

This is a comprehensive book for learning digital image processing. Using simple and practical examples it explains the fundamental techniques of image processing. It is specially designed for undergraduate students who're taking fundamental courses on digital image processing. For this reason, this guide mainly focuses on the basics of image processing. But any professional can be benefited from this book by reviewing the fundamental techniques.

What you'll learn:

  • Programming with images
  • Image manipulation and processing using ImageJ
  • Image acquisition from histograms
  • Working with different filters
  • Using morphological filters
  • Working with color images using ImageJ

Introductory Digital Image Processing: A Remote Sensing Perspective (4th Edition) (Pearson Series in Geographic Information Science)

Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. This book is suitable for For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects.

Design for Embedded Image Processing on FPGAs

This book provides a bridge between algorithms and hardware and very useful for those with software backgrounds to understand efficient hardware implementation. It shows how using  Field programmable gate arrays (FPGAs) can help you to avoid the problem of embedded image processing. FPGAs are flexible, fine-grained hardware that can readily exploit parallelism within many image processing algorithms. With this guide, you'll learn the techniques for mapping an algorithm onto an FPGA implementation, considering timing, memory bandwidth and resource constraints, and efficient hardware computational techniques.

What you'll learn:

  • Introduction to Field Programmable Gate Arrays
  • Hardware-based and Software-based language
  • Different design process for algorithm
  • Various mapping techniques
  • Point operation on single and multiple images
  • Histogram operations Including Greyscale Histograms and Multidimensional Histograms
  • Using different filters such as liner filters, non-linear filters, color filters, rank filters, and morphological filters
  • Geometric and linear transforms
  • Blob detection and labeling
  • Testing, tuning and debugging

The Image Processing Handbook, Seventh Edition

This book is considered as one of the best introduction to computer-based image processing, It covers both two-dimensional (2D) and three-dimensional (3D) imaging techniques. It features a greater range of computationally intensive algorithms for image processing. With this book, you'll learn image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more.

What you'll learn:

  • Acquiring images
  • Methods for printing and storage of digital image
  • Correcting imaging defects
  • Image enhancement in the spatial domain
  • Processing image in frequency space
  • Segmentation and thresholding
  • Processing binary images
  • Image and feature measurements
  • Image comparison with correlation, classification, identification, and matching
  • 3D image processing and measurement
  • Imaging surfaces

Image Processing and Analysis (Activate Learning with these NEW titles from Engineering!)

Image Processing and Analysis is a comprehensive guide to learn digital image processing. It is especially suitable for the beginners on this topic. This book provides a detailed presentation of the most fundamental topics in this field in a clear and concise manner. This book effectively balances key topics from the field of image processing in a format that gradually progresses from easy to more challenging material and reinforces the fundamental understanding of the core concepts. With this guide, you'll be able to understand the basic principles of image processing as well as more complex analyzing methods.

What you'll learn:

  • Fundamentals of imaging
  • Point and geometric transformations
  • Binary image processing
  • Spatial domain filtering
  • Frequency domain processing
  • Lossless and lossy compression
  • Working with image coloring
  • Segmentation and classification of images
  • Working with stereo and motion

Digital Image Processing Using MATLAB, 2nd ed.

Digital Image Processing Using MATLAB is the first book to offer a balanced treatment of image processing fundamentals and the software principles used in their implementation. The book integrates material from the leading text, Digital Image Processing by Gonzalez and Woods, and the Image Processing Toolbox from The MathWorks, Inc., a leader in scientific computing. The Image Processing Toolbox provides a stable, well-supported software environment for addressing a broad range of applications in digital image processing. A unique feature of the book is its emphasis on showing how to enhance those tools by developing new code. This is important in image processing, an area that normally requires extensive experimental work in order to arrive at acceptable application solutions. Some Highlights: (1) This new edition is an extensive upgrade of the book. (2) Over 120 new MATLAB image processing functions are developed, a 40 % increase over existing functions in the Image Processing Toolbox. (3) Algorithms and MATLAB functions in the mainstream of digital image processing are discussed and implemented, including: Intensity transformations; spatial filtering; fuzzy image processing; filtering in the frequency domain; image restoration and reconstruction; geometric transformations and image registration; color image processing; wavelets; image and video compression; morphology; image segmentation; image representation and description; and object recognition. (4) In addition to a major revision of the topics from the first edition, features in this edition include new coverage of: The Radon transform; image processing functions based on function-generating functions (function factories); geometric transformations; image registration; color profiles and device-independent color conversions; functions for video compression; adaptive thresholding algorithms; new image features, including minimum-perimeter polygons and local (corner) features.


Image Processing Toolbox provides a comprehensive set of reference-standard algorithms and graphical tools for image processing, analysis, visualization, and algorithm development. You can perform image enhancement, image deblurring, feature detection, noise reduction, image segmentation, spatial transformations, and image registration. Many toolbox functions are multithreaded to take advantage of multicore and multiprocessor computers. Image Processing Toolbox supports a diverse set of image types, including high dynamic range, gigapixel resolution, ICC-compliant color, and tomographic. Graphical tools let you explore an image, examine a region of pixels, adjust the contrast, create contours or histograms, and manipulate regions of interest (ROIs). With toolbox algorithms, you can restore degraded images, detect and measure features, analyze shapes and textures, and adjust color balance. The more important features are de next: • Image enhancement, filtering, and deblurring • Image analysis, including segmentation, morphology, feature extraction, and measurement • Spatial transformations and intensity-based image registration methods • Image transforms, including FFT, DCT, Radon, and fan-beam projection • Workflows for processing, displaying, and navigating arbitrarily large images • Interactive tools, including ROI selections, histograms, and distance measurements • DICOM file import and export

Raspberry Pi Image Processing Programming: Develop Real-Life Examples with Python, Pillow, and SciPy

Write your own Digital Image Processing programs with the use of pillow, scipy.ndimage, and matplotlib in Python 3 with Raspberry Pi 3 as the hardware platform. This concise quick-start guide provides working code examples and exercises. Learn how to interface Raspberry Pi with various image sensors.

What You'll Learn

  • Understand Raspberry Pi concepts and setup
  • Understand digital image processing concepts
  • Study pillow, the friendly PIL fork
  • Explore scipy.ndimage and matplotlib
  • Master use of the Pi camera and webcam