As cameras, webcams, optical sensors, and other devices become increasingly significant, we encounter a flood of vast volumes of data in the form of images and videos. Most likely, some of these photos and videos have flaws. As a result, the development of methods and tools to solve this problem has always been considered. There are several image processing tools and techniques available today. Machine learning is a new and fascinating research and testing issue incorporating learning details that can quickly detect and process data in subsequent encounters.
Combining machine learning and image processing has led to the development of an extremely efficient system in which machine learning models speed up the categorization, segmentation, and identification of images and videos.
In this article, we will describe image processing concepts, image processing tools, and the interaction between image processing and machine learning.
What is an Image?
Before beginning image processing, it is important to comprehend what an image actually is. Based on the number of pixels, an image’s dimensions (height and breadth) serve as a representation. For instance, if an image is 500 × 400 (width x height), then 200000 pixels make up the entire image. This pixel is a location on the image that assumes a certain hue, level of transparency, or colour. Typically, it appears as one of the following:
- Grayscale: Pixels are integers with values ranging from 0 to 255. (0 is completely black, and 255 is completely white).
- RGB: A pixel consists of three numbers in the range of 0 to 255. (The integers represent the intensity of red, green, and blue)
- RGBA is a modification of RGB with an additional alpha field, which stands for the image’s opacity.
Each pixel of an image must undergo a fixed series of operations during image processing. The initial series of actions are carried out pixel-by-pixel by the image processor on the image. The second operation will start once this is finished in full, and so forth. Any pixel in the image can be used to determine the output value of these procedures.
What is image processing?
Image processing refers to a set of processes performed on an image in order to achieve an optimized image or extract useful data from that image. One definition of an image is a two-dimensional function. The intensity or grey level of the image at any given pair of coordinates (x, y) is defined as the amplitude of F (r, y), where r and y are spatial (plane) coordinates. We refer to an image as digital when the x, y, and intensity values are all finite discrete quantities. Processing digital images using a digital computer is referred to as the field of digital image processing.
What is the importance of image processing?
Where image processing ends and other related fields, like image analysis and computer vision, begin is not generally agreed by authors. it is occasionally distinguished as a field in which a process’s input and output are both images. We consider this to be a restrictive and arbitrary boundary. According to this definition, even the simple task of calculating an image’s average intensity (which results in a single number) would not be regarded as an image-processing tool.
The importance of image processing originates from two main application areas. Improving visual information for human understanding and processing scene data for autonomous machine perception. Remote sensing, image and data storage for transmission in applications, medical imaging, acoustic imaging, forensic sciences, and industrial automation are all among the main applications of digital image processing. Satellite imagery is significant for tracking earth resources, geographical mapping, and forecasting agricultural production, urban population, and weather prediction.
Image processing algorithms
The most important factor in image processing is the algorithm, which must be understood. To accomplish various tasks, including digital image detection, image analysis, reconstruction, restoration, enhancement, image data compression, spectral image estimation, and image estimation, developers have been using and implementing various image processing algorithms. Algorithms are sometimes taken straight from a book or modified combinations of several algorithm functions. Different kinds of image-processing algorithms exist. The following are a few examples of image-processing algorithms:
1-Algorithm for Contrast Enhancement
- Algorithm for equalizing histograms: increasing image contrast with the help of the histogram.
- Algorithm for adaptive histogram equalization: histogram equalisation is used to adjust for small-scale contrast changes.
- Algorithm for connected-component labelling: finding and identifying disconnected regions is the goal.
2. Dithering & half-toning algorithm
The followings are examples of dithering and half-toning:
- Algorithm for diffusing errors
- Floyd-Steinberg dithering algorithm
- Dithering algorithm with orders
- Algorithm for Riemersma dithering
3. Elser difference-map algorithm
It is a search algorithm used to solve problems involving multiple constraints. Initial applications included X-ray diffraction microscopy.
4. Algorithm for detecting features
The followings are a few instances of the feature detection algorithms:
- Algorithm Marr-Hildreth: it is an algorithm for early edge detection.
- Edge detection algorithm for Canny: a variety of edges in images can be found using the Canny edge detector.
- Generalized Algorithm for the Hough transform
- Algorithm for the Hough transform
- SIFT algorithm (scale-invariant feature transform): an algorithm called SIFT is used to locate and describe local features in images.
- SURF algorithm (Speeded Up Robust Features): SURF is a dependable local feature detector.
- Algorithm for Richardson-Lucy deconvolution: This algorithm removes blur from an image.
5. Blind deconvolution algorithm
When the point spread function is unknown, it is an image deblurring algorithm similar to the Richardson-Lucy deconvolution algorithm.
6. Seam carving algorithm
The seam carving algorithm is a content-aware image resizing method.
7. Algorithms for segmentation
These specific algorithms divide an image of a digital object into two or more regions. It includes:
- GrowCut formula
- Region-growing algorithm
- Algorithm for watershed transformation
Image processing classification
Image processing algorithms are typically divided into the following categories when full images are captured:
- Low-level techniques, noise reduction, and colour augmentation
- techniques of a moderate level, such as compression and binarization
- Higher-level strategies use algorithms for detection, segmentation, and recognition to pull out semantic data from the captured data.
Fundamental Steps in Image Processing
The initial stage of image processing is image acquisition. In image processing, this stage is often referred to as pretreatment. The image must be retrieved from a source, typically one that is hardware-based.
The procedure of pulling out and emphasizing specific interesting features in a hidden image is known as image enhancement. This may entail adjusting the brightness, contrast, etc.
The practice of picture restoration includes enhancing an image’s look. Since restoration methods frequently draw from mathematical or probabilistic models of image degradation, image restoration is objective in this sense.
Colour Image Processing
Due to the enormous rise in the use of digital photographs on the Internet, the field of color image processing has been gaining prominence. This could involve digital color modeling and processing, among other things. On the other hand, improvement is personal.
Wavelets and Multiresolution Processing
Wavelets provide the framework for describing images at different levels of resolution. For the purposes of pyramidal depiction and data reduction, images are broken down into smaller components.
The amount of storage space or bandwidth needed to send an image is reduced using compression techniques. Data compression is absolutely required, especially for online usage.
Morphological Processing. The removal of picture elements by morphological processing helps convey and describe shapes.
Segmentation techniques separate an image into its individual elements or objects. Generally speaking, one of the most challenging tasks in digital image processing is autonomous segmentation. Imaging issues that call for the identification of individual objects can be successfully solved with the help of a robust segmentation approach.
Representation and Description
The output of a segmentation step, which is often raw pixel data that either defines the boundaries of a region or all the points within it, is always followed by representation and description. Extraction of qualities that produce quantitative information of interest or are essential for distinguishing one class of objects from another is the focus of description.
Recognition is the process of using an object’s characteristics to label it, such as “apple.”
Image Processing Methods Used in Machine Learning
Image processing tool is used to improve or extract useful information from an existing image. This is significant in several deep learning-based computer vision applications because such preprocessing can effectively enhance model performance. Another use, particularly in entertainment, is image manipulation, such as adding or deleting items from images. Image processing is simply one component of computer vision. Image processing tools focus on converting images from one format to another, whereas computer vision systems assist the computer in understanding and extracting meaning from images.
Image processing methods are used in many computer vision systems. A face enhancement application, for example, may utilize computer vision algorithms to recognize faces in an image and then perform image processing tools such as smoothing or grayscale filters on it.
Many innovative image processing methods use machine learning as a service model such as Deep Neural Networks to modify images for a variety of tasks such as adding creative filters, optimizing an image for optimal quality, or enhancing certain image features to maximize quality for computer vision tasks.
Best AI tools for image processing
A well-known open-source framework for deep learning as a service and machine learning as a service is Google’s TensorFlow. Thus, one can create and prepare unique deep-learning models using TensorFlow. As a result, the framework also includes several libraries that can be applied to computer concept applications and image processing work.
However, it was developed to address problems with developing and familiarizing a neural network to automatically find and classify images while contrasting the effectiveness of human perception.
The Facebook AI Research lab created the open-source machine learning and deep learning framework PyTorch. As a result, this Torch-based framework has interfaces for Python, C++, and Java.
PyTorch can be used to create applications for computer vision and natural language processing. Because of this, it also quickens the transition from research prototyping to industrial development.
It is an open-source library that uses image processing tools and machine-learning techniques. Additionally, it is a free computer vision library. For real-time computer vision applications, it has been created and nicely optimized. It also develops a transparent infrastructure.
A deep learning framework was used to address the image type and segmentation issue.
Image Processing Toolbox for MATLAB
The acronym for matrix laboratory is MATLAB. Thus, it is the name of both a programming language and a well-known platform for decoding scientific and mathematical situations. Thus, an Image Processing Toolbox (IPT) with many algorithms is delivered through this medium. It includes workflow applications for AI-based image analysis, processing, and algorithm development. The MATLAB IPT platform is not open-source. It, therefore, offers a free trial.
Computer vision from Microsoft
Microsoft offers Computer Vision as a cloud-based service. As a result, it provides access to sophisticated algorithms for information extraction and image processing.
Google Cooperative (Colab)
The cloud service Google Colaboratory (Colab) is free. As a result, it enhances coding abilities and creates applications for deep learning from scratch.
As a result, it also uses well-known libraries like Keras and TensorFlow when creating AI-based applications. This service, which is built on Jupyter Notebooks, enables AI developers to impart their knowledge and skills.
Image processing tools at Saiwa
Image processing is one of the most prominent disciplines in computer graphics, computer vision, artificial intelligence, and machine learning, and the algorithms and computations are closely connected to them. Today, the field of image processing has expanded tremendously, with numerous related techniques, methodologies, and tools being developed. At Saiwa, we provide several Online image processing tools. The following are the most significant of these services: