Image Processing overview: Latest research 2024, Applications, Benefits and more

Image processing is a method of manipulating or altering images to achieve a desired result, usually to improve visual quality or extract useful information from images. Modifying or analyzing images requires a variety of techniques and algorithms and is a fundamental component of computer vision, artificial intelligence, and many other fields. Here we provide an overview of image processing, its applications, and its benefits.

Overview of Image Processing

1. Image Acquisition: This process starts with capturing images using devices such as digital cameras, scanners, and sensors. The quality and resolution of the images obtained are very important.
2. Preprocessing: This step involves cleaning the image, removing noise, correcting distortions, and improving quality. Common preprocessing techniques include image denoising, contrast adjustment, and image resizing.
3. Image Enhancement: Enhancement techniques aim to improve the visual quality of images. These methods can sharpen edges, adjust brightness and contrast, and highlight specific features within an image.
4. Image Restoration: Restoration techniques are used to recover or repair the original image from a damaged or corrupted version. This is useful for scenarios like restoring old photos and removing scratches and dirt.
5. Image Segmentation: Segmentation involves dividing an image into meaningful regions or objects. It is often used in object detection, medical image analysis, etc.
6. Feature Extraction: In this step, relevant information is extracted from the image. This may involve extracting specific patterns, shapes or features for further analysis.
7. Object Recognition: Object recognition techniques are used to identify and classify objects in images. It is the basis of applications such as facial recognition, object tracking, and self-driving cars.
8. Pattern Matching: Image processing can be used to find patterns or templates in images. It is useful in various fields, such as character recognition and fingerprint analysis.

Deep learning has had a wide impact on various technology areas over the past few years. One of the hottest topics in the industry is computer vision, or the ability for computers to understand images and video on their own. Self-driving cars, biometrics, and facial recognition all rely on computer vision. Image processing is at the core of computer vision.

What Is an Image?

Before we get into image processing, we first need to understand what actually constitutes an image. Images are represented by dimensions (height and width) based on the number of pixels. For example, if the image dimensions are 500 x 400 (width x height), the total number of pixels in the image is 200000.

This pixel is a point on an image that takes on a particular color, opacity, or hue. Usually represented by one of the following:

• Grayscale: A pixel is an integer whose value is between 0 and 255 (0 is completely black, 255 is completely white).
• RGB: A pixel consists of three integers from 0 to 255 (the integers represent the intensities of red, green, and blue).
• RGBA is an extension of RGB with the addition of an alpha field to represent the opacity of an image.

Image processing requires a set of fixed operations performed on each pixel of the image. The image processor performs an initial set of operations on the image, pixel by pixel. Once it is completely completed, another operation starts running, and so on. The output values of these operations can be calculated at any pixel in the image.

What is image processing?

Image processing is the process of converting an image into digital format and performing certain operations to extract useful information from it. Image processing systems typically treat all images as 2D signals when applying some predefined signal processing methods.

Types of Image Processing

There are five main types of image processing:

• Visualization: Find objects that are not visible in the image
• Recognition: Distinguish or detect objects in the image
• Sharpening and restoration: Create an enhanced image from the original image
• Pattern recognition: measure the various patterns around the objects in the image
• Retrieval: Browse and search images from a large database of digital images that are similar to the original image

Components of Image Processing

Computer

Image processing systems use general-purpose computers ranging from PCs to supercomputers. In some cases, computers built specifically for particular applications are used to achieve specific levels of performance.

Hardware for Specialized Image Processing

It consists of a digitizer and hardware capable of performing basic operations, such as an arithmetic logic unit (ALU) that can perform arithmetic and logic operations simultaneously on the entire image.

Massive Storing

Skills are essential for applications involving image processing. There are three main types of digital storage for image processing applications: Three types of storage exist: (1) short-term storage, (2) online storage with immediate recall, and (3) archival storage characterized by infrequent access.

Camera Sensors

It means perception. The main function of an image sensor is to collect incident light, convert it into an electrical signal, measure that signal, and output it to supporting electronics. It consists of a two-dimensional array of light sensitive components that convert photons into electrons. Images are captured with a device such as a digital camera using an image sensor such as CCD or CMOS. To collect digital images, an image sensor often requires two components. The first is the actual device (sensor) that can detect the energy emitted by the object you want to convert into an image. The second is the digitizer, which converts the output of the physical sensing device into digital format.

Image Display

The pictures are shown; that is image display.

Software

Image processing software has specialized modules that perform specific tasks.

Hard copy device

Laser printers, film cameras, thermal devices, inkjet printers, and digital devices such as optical and CDROM discs are some of the devices used to record images.

Networking

An essential component for transmitting visual data across networked computers. Image processing applications require huge amounts of data, so the most important factor in transmitting images is bandwidth.

Fundamentals of Image Processing Steps

Image acquisition

Image acquisition is the first step in image processing. This step is also called preprocessing for image processing. It involves retrieving images from a source, usually a hardware-based source.

Image correction

Image enhancement is the process of uncovering and highlighting specific features of interest hidden in an image. This may include changes in brightness, contrast, etc.

Image repair

Image restoration is the process of improving the appearance of images. However, unlike image enhancement, image restoration is performed using specific mathematical or probabilistic models.

Color image processing

Color image processing includes many color modeling techniques in the digital domain. The move has attracted attention due to the heavy use of digital images on the Internet.

Wavelets and multiresolution processing

Wavelets are used to represent images of different resolutions. The image is divided into wavelets or small regions for data compression and pyramid representation.

Compression

Compression is a process used to reduce the storage required to store images or the bandwidth required to transmit images. This is especially true when images are used on the Internet.

Morphological processing

Morphological processing is a set of processing operations to transform an image based on its shape.

Division

Segmentation is one of the most difficult steps in image processing. It involves dividing an image into its component parts or objects.

Expression and explanation

After the segmentation process the image is divided into regions, each region is represented and described in a format suitable for further computer processing. Representation is related to image characteristics and regional characteristics. Descriptions extract quantitative information that helps distinguish one class of objects from another.

Recognition

Identification assigns labels to objects based on their description.

Applications of Image Processing

1. Medical Image Processing: Image processing plays an important role in medical diagnosis such as X-ray analysis, MRI, CT scan and identifying abnormalities in medical images.
2. Remote sensing: Analysis of satellite images for environmental monitoring, disaster management and land use planning.
3. Computer Vision: Image processing is the backbone of computer vision applications such as facial recognition, object detection, and gesture recognition.
4. Security and surveillance: Used for video surveillance, analysis of CCTV footage, and facial recognition in security applications.
5. Entertainment: The film and gaming industries use image processing for special effects, image editing, and improving visual quality.
6. Industrial Automation: Image processing is used for quality control, defect detection, and robotics in manufacturing.
7. Document Analysis: OCR (Optical Character Recognition) relies on image processing to convert printed or handwritten text into machine-readable text.
8. Astronomy: Analyze and enhance astronomical images to study celestial objects and phenomena.

Image Processing Techniques

Image processing can be used to improve image quality, remove unwanted objects from images, and even create new images from scratch. For example, you can use image processing to remove the background from an image of a person, leaving only the subject in the foreground.

Image processing is a vast and complex field, and there are many different algorithms and techniques that can be used to achieve different results. This section focuses on some of the most common image processing tasks and how to perform them.

Image Enhancement

One of the most common image processing tasks is image enhancement, or improving the quality of an image. It has important applications in computer vision tasks, remote sensing, and surveillance. A common method is to adjust the contrast and brightness of the image.

Contrast is the difference in brightness between the brightest and darkest parts of an image. Increasing contrast brightens the entire image and makes it easier to see. Brightness is the overall brightness or darkness of the image. Increasing the brightness makes the image brighter and easier to see. Contrast and brightness can be adjusted automatically or manually in most image editing software.

However, adjusting image contrast and brightness is a basic operation. An image with perfect contrast and brightness may appear blurry when upscaled due to low pixels per square inch (pixel density). To solve this problem, a relatively new and more advanced concept called image super-resolution is used. In this concept, a high-resolution image is obtained from a low-resolution image. To achieve this, deep learning techniques are widely used.

Image Restoration

Image quality may degrade for many reasons. Especially photos from a time when cloud storage was not so common. For example, images scanned from hard copies taken from old instant cameras often contain scratches.

Image restoration is particularly attractive because the field’s advanced technologies have the potential to restore damaged historical documents. Powerful deep learning-based image restoration algorithms have the ability to uncover most of the information missing from torn documents.

For example, image inpainting falls into this category and is the process of filling in missing pixels in an image. This can be done using a texture synthesis algorithm that synthesizes new textures to fill in the missing pixels. However, deep learning-based models have become the de facto choice due to their pattern recognition capabilities.

Image Segmentation

Image segmentation is the process of dividing an image into multiple segments or regions. Each segment represents a different object in the image, and image segmentation is often used as a preprocessing step for object detection.

There are many algorithms available for image segmentation, but one of the most common methods is to use thresholding. For example, binary thresholding is the process of converting an image into a binary image, where each pixel is either black or white. The threshold is chosen such that all pixels with brightness levels below the threshold become black and all pixels with brightness levels above the threshold become white. This allows objects in the image to be represented by separate black and white areas, thus segmenting them.

Modern techniques use automated image segmentation algorithms using deep learning for binary and multi-label segmentation problems. For example, PFNet (Positioning and Focus Network) is a CNN-based model that solves the problem of hidden object segmentation. It consists of two main modules. One is a positioning module (PM) designed to locate the object (mimicking a hunter trying to determine the rough location of his prey). The Focus Module (FM) is designed to perform a detection process during hunting to focus on unclear areas and refine the initial segmentation results.

Object Detection

Object detection is the task of identifying objects in images and is often used in applications such as security and surveillance. Various algorithms can be used for object detection, but the most common approach is to use deep learning models, specifically Convolutional Neural Networks (CNN).

CNN is a type of artificial neural network specifically designed for image processing tasks. Its main convolution operation allows the computer to “see” patches of an image rather than processing them one pixel at a time. A CNN trained for object detection outputs a bounding box (see image above) with class labels indicating where the object is detected in the image.

Image Compression

Image compression is the process of reducing the file size of an image while maintaining its quality. This is done to save storage space, to run image processing algorithms, especially on mobile and edge devices, or to reduce the bandwidth required to transmit images.

Traditional approaches use lossy compression algorithms that slightly degrade image quality to reduce file size. For example, the JPEG file format uses the discrete cosine transform for image compression.

Modern methods of image compression involve the use of deep learning to encode the image into a low-dimensional feature space and restore it at the receiving end using a decoding network. Such models are called autoencoders and consist of an encoding branch that learns an efficient encoding method and a decoder branch that attempts to losslessly recover the image from the encoded features.

Image Manipulation

Image manipulation is the process of modifying images to change their appearance. This may be desirable for several reasons, such as removing unnecessary objects from the image or adding objects that are not present in the image. Graphic designers often do this to create posters, films, etc.

Image Generation

Synthesizing new images is another important task in image processing, especially deep learning algorithms that require large amounts of labeled data for training. Image generation methods typically use another unique neural network architecture: Generative Adversarial Networks (GAN).

A GAN consists of two different models: a generator that creates a synthetic image, and a discriminator that attempts to distinguish the synthetic image from the real image. The generator attempts to synthesize realistic-looking images to fool the discriminator, which trains the discriminator to better judge whether an image is synthetic or real. This allows the adversarial game generator to generate photorealistic images after multiple iterations, which can be used to train other deep learning models.

Image-to-Image Translation

Image-to-image transformation is a type of visualization and graphics problem in which the goal is to learn a mapping between input and output images using a training set of aligned image pairs.

Benefits of Image Processing

1. Improves image quality: Image processing improves image quality, making the image clearer, clearer and more attractive.
2. Automation: Automate time-consuming and error-prone tasks that would otherwise be done manually, such as object recognition and defect detection.
3. Information extraction: Valuable information can be extracted from images and used for decision making and analysis.
4. Medical Diagnosis: In the medical field, image processing helps in early detection and non-invasive diagnosis of diseases.
5. Cost Reduction: The need for manual labor is reduced and process efficiency is improved, leading to cost savings in various industries.
6. Scientific Research: Image processing is important in scientific research, allowing researchers to analyze and visualize data effectively.
7. Enhanced Security: Enhanced security through facial recognition, fingerprint analysis, and object tracking.
8. Creative Expression: The arts and entertainment industry allows for creative expression and the development of visually stunning effects.

Summary

In short, image processing is a versatile field with many applications ranging from improving image quality to enabling advanced automation and analysis in a variety of fields. Benefits include improved image quality, automation of tasks, and extracting valuable information for decision-making and research.

The age of information technology in which we live has made visual data widely available. However, it requires a lot of processing to transmit it over the Internet or for purposes such as information extraction or predictive modeling.

Advances in deep learning technology have led to CNN models specifically designed for image processing. Since then, many advanced models have been developed that address specific tasks in the image processing field. We reviewed some of the most important techniques in image processing and common deep learning-based methods that address these problems, from image compression and enhancement to image synthesis.

Recent research has focused on reducing the need for ground truth labels for complex tasks such as object detection and semantic segmentation by employing concepts such as semi-supervised learning and self-supervised learning. This makes the model more suitable for a wide range of practical applications.‍

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