What Is Image Processing: A Complete ML and AI Image Guide

A complete guide to Image Processing. Get to know what is Image Processing and understand the benefits, applications, components, etc. of digital image processing:

Deep learning had a deep impact on technology over the last few years. One of the hottest topics in today’s industry is computer vision. We define the term computer vision as the ability of computers to understand videos and images.

The process of transforming the image into digital form and creating certain operations to retrieve meaningful information from it is known as Image Processing. With the assistance of computer vision, face recognition, biometrics, and cars can be made self-driven.

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There are distinct types of image processing, i.e. visualization, recognition, pattern recognition, retrieval, sharpening, and restoration. On applying certain signal processing methods, the images are considered 2D signals.

What is Image Processing

Image Processing is the process of converting an image into a digitized form. Many operations are done on the image to extract and capture useful information from the image. With the usage of computers, the images are processed through an algorithm.

Before deep diving into what is digital image processing, we need to understand what exactly an image consists of. Based on the number of pixels, the image is represented by its dimensions (height and width).

For example, if the dimensions of an image are 500 * 400, then the total number of pixels is the multiplication of its height and width, i.e. 200000.

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A Pixel is a specific point in an image that defines a particular color, shade, or opacity. Depending upon the number of pixels, the color or opacity of an image can be determined.

• Grayscale: A pixel having an integer value between 0 and 255. 0 integer represents black color while 255 integer represents white color.
• RGB: A pixel is always a combination of three integers between 0 and 255. The intensity of all the colors, i.e. red, green, and blue is represented by an integer.
• RGBA: It is an extension of RGB which represents the opacity of an image.

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In digital processing, the signals are captured and then they are translated into digital form by the process of digitalization.

Here are a few steps described for digitally processing the images:

#1) Sampling and Quantization: A digitizer sample and quantize the analog video signals. To convert the images into digital form, we need to convert the continuous data into digital form. There are two steps for digital image processing, i.e. Sampling and Quantization.

• Sampling: Through sampling, the analog signals are converted into discrete values. It is a process of recording analog signals at regular intervals of time. The spatial resolution of a digitized image can be attained through sampling.
• Quantization: The term Quantization means the transition between continuous values of images’ function and its digital equivalent. We can recognize the number of gray levels in the digitized image as Quantization.

#2) Resizing Image: Through distinct ways, the image can be resized. It can be done either by increasing or decreasing the total number of pixels, zooming, or remapping. For enhancing or increasing the quality of pixels, zooming needs to be done. While for remapping an image, rotation of an image or lens distortion is required.

#3) Aliasing and Image Enhancement: For enhancing the frequencies of signals, either of digital photographs or sound, the aliasing of an image is done. Through aliasing, the signal is sampled at less than twice the highest original frequency of the signal. The Aliasing effect makes it hard to distinguish different signals because they are sampled together.

4) Arithmetic and Logical Operations: By the logical operators, two or more images can be combined. Image arithmetic is one of the standard arithmetic operations. In a pixel-by-pixel way, the logical or arithmetic operators can be applied.

The value of pixels in the output image depends upon the corresponding input image. The process of adding the images through operators, i.e. NOT, AND, and OR is straightforward and fast.

#5) Spatial Domain Filtering: For enhancing or modifying an image, filtering is the best thing to apply. Spatial is a form of impulse response filtering. For example, the data received from satellites and space probes can be filtered. Apart from it, the raster can be removed from a scanned image or from a television picture through spatial domain filtering.

Image Transformation

Through the spatial coordinates (x,y) or (x,y,z) the image can be obtained. The transformation of an image from one domain to another domain can be easily done through coordinates. An image is a function f(x,y) of two continuous variables, x and y.

For processing it digitally, sampling and transforming it into a matrix of numbers is essential. The overall goal of processing the images digitally is to increase the contrast, remove the noise, or decrease the blur effect. Adobe Photoshop and MATLAB are the most common software for processing images digitally.

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There are two types of image transformations that can be performed.

#1) Fourier Transformation

Through the Fourier transformation, the intensity of the image is transformed into frequency variation. Following it, the frequency variation is transformed into the frequency domain. For the slow varying intensity images, the Fourier transformation technique can be easily applied.

For example, the background of a passport-size photograph can be represented as a low-frequency component. On the other hand, the edge can be represented as a high-frequency component. By leveraging Fourier transformation, the edges of an image can be fine-tuned.

Two-Dimensional Fourier Transform

The two-dimensional Fourier transformation is the series expansion of an image function in terms of cosine range.

Matrix Notation

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The above image is an example where a blurred image is transformed into a Fourier. Through Fourier transforms the image is represented as a sum of complex exponentials, such as phases, frequencies, and magnitudes. In image processing applications, Fourier transformation plays a critical role that includes compression, restoration, analysis, and enhancement.

Some of the key properties of Fourier Transformation are:

• Periodic Extension
• Conjugate Symmetry
• Circular Convolution
• Symmetric Unitary
• Fast and Sampled Fourier

#2) Discrete Cosine Transformation (DCT)

With the assistance of coefficients, the information about the pixels of an image is transferred. Some coefficients contain more information, while others contain minimal information. After the information is passed about the image pixels, the coefficients can be removed.

No information is lost when the coefficients are removed. The size of an image file can also be reduced or compressed using the DCT methodology. In the standard format of jpeg or mp3, the images or sounds are found.

Discrete Cosine Transformation is mainly used in lossy image compression as it stores a large amount of information in a very low-frequency component.

• One Dimensional Discrete cosine transformation

• Two Dimensional Discrete cosine transformations

The discrete cosine transformation is one of the most common signal transformation methods. There are many variants of DCT, like DCT I, II, III, IV, V, VI, VII, VIII. All of them have their distinguished properties and definitions. DCT II is the most common for image processing and compression.

Here are a few properties of Discrete Cosine Transformation:

• Fast Transformation
• Highly Correlated data
• Real and Orthogonal

Fundamental Steps of Image Processing

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In the above diagram, the fundamental steps of how the digital image is processed are described. The process includes acquisition, enhancement, restoration, color processing, multi-resolution, compression, morphological, and segmentation steps.

All the fundamentals of digital image processing steps are briefly described below:

• Image Acquisition: Capturing an image through a digitalized camera and importing the existing image to a computer through mobile, tablet, etc is termed image acquisition.
• Image Enhancement: The process involves enhancing the quality of the image by reducing the artifacts, reducing noise, or increasing the contrast. Hence, the quality of an image is improved.
• Image Restoration: By restoration, the degradation in an image as blur, noise, or distortion is completely removed.
• Color Image Processing: This process includes processing the image in the digital domain and applying color modeling to it. In today’s digitalized world, the requirement for colored images is very high.
• Wavelets & Multi-Resolution Processing: Through various degrees of resolution, the image is represented. In the pyramidal representation and data compression, the entire data is divided into small regions.
• Compression: For reducing the storage space, the images are compressed. Through this process, the size of an image is reduced.
• Morphological Processing: In this part of the process, specific tools are used to extract the components of an image.
• Segmentation: Dividing the image into segments or regions is known as segmentation. Corresponding to specific object features available in the image, the segments are created.

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Datasets for Computer Vision Training

Through distinct ways and methods, the datasets can be used for computer vision training. By leveraging various deep learning techniques, through different types of data distinct models can be trained. The datasets are divided into three categories i.e. Image Processing, Audio/Speech Processing, and Natural Language Processing.

• Indoor Scene Recognition: For training a model that recognizes indoor scenery, this type of specialized dataset is highly recommended. It contains around 15620 images that are distinguished into 67 indoor categories.
• Google Open Images: This is one of the largest datasets that contain millions of images with annotated labels across 600 categories and 9 million URLs.
• YouTube-8M: In terms of videos, it is one of the largest datasets. Having a million YouTube video IDs, it contains more than 3800 + annotated visual entities. All the entities are excluded that aren’t localizable on movies or on TV series.
• ImageNet: The ImageNet is organized as per the WorldNet hierarchy and each node is depicted in thousands of images. ImageNet is an ideal dataset for all new algorithms.
• Labelme: This dataset consists of computer science images and specifically artificial intelligence. Labelme contains 187240 images each of 150 GB size. Distinct across 658,992 labeled objects, it contains 62,197 annotated images.
• MS COCO: It is one of the most detailed datasets, as it features large-scale object detection, segmentation, and captioning dataset. It supports over 2,00,000 labeled images. With the amazing features of context recognition, 5 captions per image, 25 GB compressed size of the file, 91 distinct categories, 1.5 million object instances, super-pixel stuff segmentation, 80 object categories, and 250000 people with key points MS COCO dataset is a complete package.
• Visual QA: For open-ended questions, Visual QA is popularized. It contains around 265000 images. With the features like 10 ground truth answers per question, an automatic evaluation metric, 3 questions per image, and 3 plausible answers per question, Visual QA is also a good dataset and can be leveraged.
• CIFAR-10: In CIFAR-10, there are around 60,000 images of 32*32 that are separated into 10 distinct classes. The dataset consists of five training batches and one batch contains 10,000 images. The size of an image should be 170 MB.

Ready-Made Solutions

For solving a specific task, there are many software tools that are created for resolving a particular problem in detecting objects or images. All these ready-made solutions are open-source repositories. YOLO, R-CNN, Mask R-CNN, MobileNet, and SqueezeNet are the most popular object detection algorithms.

Let’s see each one of them in detail:

• YOLO: YOLO stands for You Only Look Once. For real-time object detection, it is one of the most popular commercial products available. The first version of the YOLO detector was released in 2016. After that, with the release of each version, the efficiency and performance of YOLO has been increased.
• SSD: SSD is a single-shot detector that can detect multiple classes within a single shot. With the leverage of a deep neural network, it easily detects the objects in images. The SSD can be easily integrated and trained with software systems that require object detection components.
• MobileNet: For determining the location of an environment, facial analysis, and for recognition through a smartphone, MobileNet is a set of computer vision algorithms optimized for mobile devices. MobileNet provides a detection network that can be used as a single-shot multi-box detection and for object detection tasks.
• Mask R-CNN: Mask R-CNN can be used for predicting object masks and also for bounding box recognition.
• Speech2Face: By audio recording a voice, the image of a person’s face can be generated. Through the spectrogram, input is taken. With the usage of the spectrogram, the full face of a person can be generated as an output.
• Fritz: Without transferring any data, Fritz runs on both iOS and Android mobile devices. The models can be easily updated using the Fritz application. Porting the models to other frameworks can also be done.
• Computer Vision Annotation Tool: With computer vision, there is no requirement for any installation. Through this interactive tool, marking videos and photos can be effortlessly done. In the shapes of points, polygons, rectangles, and polylines the marking of videos/photos can be easily achieved.
• EDVR: The frame of videos can be reframed using EDVR. Through EDVR, the sharpness and content of blurry frames in video recording can be restored. Blurred frames can be used as an input, while the restored frames without any blur are expected as an output in EDVR.
• DeepView: Based on convolutional neural networks, the 3-dimensional view of an image can be restored from a couple of photos. Through DeepView, the resolution of an image can be increased up to 8 times. Better-quality of face images without any distortion is also easily attainable through DeepView.
• 3D-BoNet: It is an end-to-end neural network that accepts 3D images as input and delivers boundaries of recognized objects as output. Segmentation of objects and solving the instance segmentation is 10 times better than other approaches in 3D-BoNet.

Image Processing With Keras

Keras API library is a deep learning library that provides methods for loading, preparing, and processing images. Here, we are going to discuss a few steps as to how the images are processed with Keras.

#1) Load an image: load_img is the function used in Keras for uploading an image. The format of an image is JPEG in accordance with the size of (6000, 4000). After uploading the image, information can be fetched through it.

Code for loading an image:

#2) Process an image: We can perform a number of actions on an image before processing it. Like that resizing the image, changing the color, or converting it into an array. All these steps need to be done before sending the images for training the model.

#3) Convert the image into an array and vice-versa: Through the img_to_array function, the image can be converted into an array. While array_to_img is the function for converting the array into an image. Information of images like shape, type, and image array can be easily accessed.

#4) Changing the color of an image: For converting the colorful images into grayscale, need to set grayscale = True in load_img (). Through save_img (), the converted images can be saved.

#5) Processing image dataset: For loading the image from the dataset, load_data () function can be used. With the leverage of mnist dataset, the A_train and B_train can be used for training the models. The A_test and B_test can be used for testing purposes. Using reshape () function, the size of an image can be altered.

Components of Digital Image Processing

While processing the image for digital transformation, distinct elements are combined in the image processing system. With the usage of different computer algorithms, digital images are processed.

Here, we have briefly described all the components:

• Computer: A normal computer can be used for image processing. But, it is recommended to get a supercomputer for processing the images. By processing the images through a supercomputer, the quality and performance of images can be increased to a great extent.
• Camera Sensors: The primary function of camera sensors is to collect light and convert them into electrical signals. Before delivering the output to the supporting electronics, the measurement of the signal is done. The images are captured through sensors like CCD and CMOS.
• Massive Storing: There are three distinct types of storing that can be used for massively storing images. Short-term storage, archive storage, and online storage for quick recall are some of the few storage options.
• Image Display: The pictures are depicted through any digitally functioning device.
• Software: For carrying out specific functions, the image processing software consists of specialized modules.
• Hardcopy Equipment: Inkjet printers, heat-sensitive equipment, laser printers, and film cameras are a few examples for recording pictures.
• Networking: Networking is the most important component for transferring data. Therefore, bandwidth plays a vital role in image processing applications, as it requires a lot of data.

Special Effects in Image Processing

• Ringing Effect in Image Processing: For closing the sharp edges in photos and videos, the ringing effect is used while processing the image. According to mathematical approaches, the unpleasant ringing effect is called as Gibbs phenomenon. Due to the loss or distortion of high-frequency information, an artifact appears as rippling ripples.
• Importance of Phase in Image Processing: The phase contains information about the positions of features placed in an image. In a wave cycle, the repeated waveform describes the timings and position of a particular point. Specifically for the photos, the transformation needs to be reversed and multiplication of the Fourier domain needs to be done.

Applications of Digital Image Processing

Digital image processing varies across a vast range of applications. As technology evolves from time to time, the usage of digital image processing is also continuously increasing.

• Medical: Gamma-ray imaging, PET scan, UV imaging, Medical CT and X-Ray Imaging are some common applications of the medical field where digital image processing is done. Formatting, reconstruction, high-quality image distribution, fast image storage, and controlling viewing can be done easily using digital image processing.
• Remote Sensing: Leveraging satellites, distinct parts of the Earth can be explored and activities happening in space or Earth can be easily captured.
• Machine\Robot Vision: Based on the vision, the robots can view and identify things. For example, in the hurdle detection root and line follower robot, through the robotic machines, the work is being processed. With the advancement of robot machines, the labor work done by humans has been highly reduced.
• Pattern Recognition: With a combination of artificial intelligence and image processing, image recognition, handwriting recognition, and other computer-related diagnosis can be easily done.
• Video Processing: With a collection of pictures or frames, the movement of pictures can be made faster. It involves the reduction in noise, color space conversion, motion detection, and frame rate conversion.
• Traffic Sensing Technologies: The video image processing system consists of a telecommunication system, an image processing system, and an image capturing system. The VIPS has several detection zones. Whenever a vehicle enters the zone, the input is taken. For sensing the traffic in a particular area, these detection zones are created. Apart from detecting the vehicle, it records the license plate, monitors the speed of the vehicle, and categorizes the type of automobile. Therefore, it proves to be very much helpful for controlling and monitoring traffic.

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In the above image, through the camera, when the vehicle enters the detection zone, the vehicle number, speed of the automobile, and type of conveyance can be captured.

Digital Image Processing Chain

In a few steps, how the digital image chain processing is carried out is described. Starting from capturing the image to getting it transformed into the desired state. Finally, the received output can be stored in any mobile, computer, laptop, tablet, or any other digital device.

The entire process of converting an image involves the conversion of an image from one format to another, blurring, detecting edges, and retrieval.

Step #1: The camera sensor produces a raw color filter array, which acts as an input for processing the digitalized chain.

Step #2: For adjusting the illuminant color, scene edge content, and brightness level of an image auto exposure, autofocus, and automatic white balancing is done through algorithms. These primary steps are referred to as A*.

Step #3: After the CFA image has been read from the sensor device, the elimination of noise from the image is being processed.

Step #4: After the reduction of noise, the white balancing of an image is done. Irrespective of the illuminating light, the white paper should look white.

Step #5: By executing the demosaicing step, two missing color channels are restored as three color channels for describing a color.

Step #6: By color correction the non-standard color data is altered into standard color space.

Step #7: For increasing the quality of an image, amplification of the real image is done. It increases the edge enhancement of an image.

Step #8: By doing mathematical redundancies the size of an image file is reduced.

Step #9: Before the last step, compression of the image is done. For confirming that no information is lost during the entire processing, the compression step is done.

Step #10: At the end, an RGB image is produced as an output. The RGB image can be stored in any device, such as a phone, tablet, computer, or any other digital device.

In the above figure, the working of a digital processing chain is explained. The input is given through a camera and the output is as an RGB image. All the detailed steps and procedure is mentioned in the diagram.

Key Benefits of Digital Image Processing

Irrespective of any field of work, image processing technology is rapidly growing and its implementation is very easy and hassle-free. Here, are a few key benefits of image processing:

• Improved Image Quality: Through image processing, it has become very easy to store or retrieve any images in any supporting format. For human interpretation, the images used are of good quality.
• Ease of Customization: The pixels of an image can be manipulated to any specific contrast and to any density. For the interpretation by machines, the information in an image can be processed and extracted.
• Increased Accuracy: Through digitized image processing, algorithms more accurate and precise results can be achieved which can’t be gained through human efforts.
• Increased Efficiency: Leveraging digital image algorithms the images can be processed faster in a short duration of time using a large set of data. The images can be transferred easily to any third-party user without any hassle.
• Automated Image-Based Tasks: Automatically many image-based tasks can be digitally processed. For example, pattern detection, measurement, or object detection can be done easily and fastly through automated image tasks.

Processing Image

Image processing is a signal processing in which the input is an image and the output may be an image or any feature associated with it. In the field of computer science and engineering, image processing is one of the fastest and most rapidly growing technologies. Analog and digital processing are two distinct methods for processing images digitally.

Here, we are discussing a few image processing steps that need to be considered while processing an image:

• Image Acquisition Tools: Through image acquisition tools, the images are imported to a device such as a computer, tablet, phone, or any other digitalized device.
• Alteration of Image: Following it, analysis and manipulation of the image is done.
• Final Result: On the basis of analysis, the output can be an altered image.

#1) Analog Image Processing: In the analog processing of signals, the images are manipulated or converted into electrical signals. The signals can be periodic as well as non-periodic. Through analog image processing, only two-dimensional signals can be processed. For example, printouts, photographs, medical images, and television images are all examples of analog image processing.

#2) Digitized Image Processing: Through digitized processing techniques, the manipulation of digital images by computers can be done. Pre-processing, enhancement, display, and information extraction are some of the types of digital image processing. Some examples of digital image processing devices are mobile phones, voice detection machines, CDs, satellites, battlefields, etc.

Image processing requires a fixed sequence of operations that can be performed at each pixel of an image. The operations can be performed using a pipeline of processors. The output of one pipeline is the input of another pipeline.

For breaking down operations into sub-operations, the pipelines can be structured into distinct ways to take out the maximum output.

Differences Between Analog and Digital Image Processing

There are a few differences between analog and digital image processing. Here we are highlighting some of the big differences that can create a huge impact while processing the images.

Installation of Libraries

For installing the libraries related to image processing, a recent version of Python 3.x needs to be installed on the computer or laptop. The code used in the libraries can be made to run on Google Colab or any other cloud service having Python as an interpreter.

1. Open CV: The term Open CV stands for Open Source Computer Vision Library. Over 2000, optimized algorithms are contained in the OpenCV library for computer vision and machine learning. For converting one color image to another, performing threshold, extracting foreground, building image pyramids, image segmentation, smoothing of images, and for performing morphological operations on images the Open CV library can be used.
2. Scikit image: Leveraging machine learning built-in functions, Scikit is an open-source library used for image pre-processing. With a few functions, complex operations can be easily done on an image. For rotating, rescaling, implementing Gaussian smoothing, threshold operations, edge detection, or for morphological operations, the Scikit library can be leveraged.
3. PIL/Pillow: PIL stands for Python Image Library. PIL supports a wide range of image formats. PPM, JPEG, PNG, BMP, TIFF, GIF, and PNG image formats are supportable on PIL. It is one of the most powerful libraries, as it supports grayscaling, cropping, resizing, and rotating functions.
4. NumPy: For extracting features, flipping images, and analyzing them NumPy library can be used. Using NumPy library multidimensional arrays, the images can be represented. For example, a color image is a combination of three dimensions NumPy array.
5. Mahotas: Mahotas is an independent module and most of the algorithms are supported in C++ language. With more than 100 functions, it is a computer vision and image processing library. Through Mahotas reading of an image, mean calculation, local maxima, eccentricity or dilation and erosion of an image can be done very easily.

How to convert an image into its equivalent grayscale version

1. Upload a photo and convert it into JPG or PNG format: You can easily upload a photograph or drag and drop it to the image editor from your computer, Google Drive, etc.
2. From the top menu, the filters and effects can be selected for an image: With the usage of image filters like sepia, vintage, B&W, and photo effects such as brightness, contrast, or saturation of the image can be created the perfect filters and effects.
3. Adjust the slider for converting the image to grayscale: With the help of a grayscale converter, images can be converted into stunning graphics with virtually appealing designs.
4. Modify the level of grayscale tone: Leveraging the grayscale tone slider, the vintage look of an image can be adjusted. The distractions in the image can be completely removed. Following it, customized exposure to grayscale needs to be given to an image.
5. Download the image in the format of JPG, PNG, SVG, or PDF: Through the image editor, the images can be easily downloaded and can be incorporated into any project.

Machine Learning Image Processing

With the assistance of deep learning algorithms, the machines are first trained. After training, the machines can detect the objects and are able to resemble them with images. By leveraging computer vision, the images can be manipulated or transformed. An ideal database is created for machine learning algorithms.

For predicting accurate results, machine learning algorithms need a high amount of quality data. Before processing the images, there are some pre-processing steps that need to be taken care of:

• Formatting: The format of all the images should be the same. Either it should be a png, jpg or anything else. But, the formatting of all images should be unique and similar to each other. Only when the images are of the same format, the machine learning models can be trained for classification or extraction of content.
• Eliminating Unwanted Space: Second, cropping and eliminating the unwanted things or space from the photos is the step of execution.
• Transformation: The images are first transformed into numbers. After that, through the algorithms, the machine learning models are trained from it.

Depending upon the resolution of an image, the input images are selected from an array of pixels. Decision Trees, Neural Nets, Nearest Neighbors, Genetic Algorithms, and Bayesian Nets are some of the most popular algorithms used for training and classifying.

AI Image Processing

In the last four years, the number of AI adopters across distinct industries has grown up to

270%. Due to the advancement in AI, software developers can design software that can observe, understand, describe, and recognize the photos or content in the video with good accuracy and precision results. By leveraging machine learning, the images can be processed.

Based on algorithms, the analysis and manipulation of digitized images is done for overall improving the quality and performance. Using image recognition algorithms, distinguished patterns can be recognized.

For automated processes, better quality results, improved accuracy, fast speed, and cost savings, AI image processing is having huge demand in the market.

For example, the classification of objects i.e. bicycles, people, automobiles, and other things can be easily done through AI.

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Frequently Asked Questions

Conclusion

In today’s world, image processing is one of the most demanding technologies. It is the most evolving computer science and engineering technology. With the usage of image processing advanced technology, the enhancement and up-gradation of an image can be done effectively and efficiently.

In the article, we have deeply looked at and understood how the images are processed for training models, extracting information, etc. Through the various techniques and tools we have learned how the images are compressed, enhancement of an image is done and overall image synthesis can be achieved.

Parallel and distributed computing paradigms are the upcoming things for improving the responses in image processing results.