There are two types of image processing methods: analogue image processing and digital image processing.
Hard copies, such as printouts and photographs, can benefit from analog image processing. When using these visual techniques, image analysts employ a variety of interpretation fundamentals.
Digital image processing techniques aid in the manipulation of digital images through the use of computers. Pre-processing, enhancement, and display, as well as information extraction, are the three general phases that all types of data must go through when using digital techniques.
Digital image processing is a technique for performing operations on images in order to improve them or extract useful information from them.
It is a type of signal processing in which the input is an image and the output can be an image or image-related characteristics/features.
Image processing is one of today’s fastest growing technologies. It is also a core research area in engineering and computer science.
The three basic steps in image processing are as follows:
- Importing the image using image acquisition software.
- Image analysis and manipulation;
- output, which can be an altered image or a report based on image analysis.
there are different techniques used for digital image processing
- Image Acquisition
The first step in any image processing system is image acquisition.Image acquisition is important in image processing because if the images are not acquired properly, the various image processing techniques may be ineffective, even if various enhancement techniques are present.
The overarching goal of any image acquisition is to convert an optical image (real-world data) into an array of numerical data that can then be manipulated on a computer.
Image acquisition is accomplished through the use of appropriate cameras. We use various cameras for various applications.
When we need an X-ray image, we use an X-ray sensitive camera (film). In order to obtain an infrared image, we employ cameras that are sensitive to infrared radiation.
We use cameras that are sensitive to the visual spectrum to capture normal images photos, for example.
2. Image Preprocessing
Image preprocessing is a critical step in image processing and computer vision. The goal of image preprocessing is to improve image data by enhancing some features while suppressing others. The enhancement of features is dependent on the application.
Image data recorded by sensors on a satellite contain errors related to pixel geometry and brightness values. These errors are corrected in image preprocessing using appropriate mathematical models, which can be either definite or statistical.
Image preprocessing also includes basic operations like noise reduction, contrast enhancement, image smoothing and sharpening, and advanced operations like image segmentation.
Preprocessing techniques are classified as
- pixel brightness transformations,
- geometric transformations,
- preprocessing techniques that use the processed pixel’s local neighborhood, and
- image restoration that necessitates knowledge of the entire image.
3. Image Enhancement
Because of the limitations of imaging subsystems and lighting conditions while capturing an image, images captured by some conventional digital cameras and satellite images may lack contrast and brightness.
Image enhancement is one of the simplest and most appealing methods for overcoming this difficulty. It enhances some features that are hidden or highlights certain features of interest for subsequent image analysis.
We can divide image enhancement techniques into two categories.
- spatial domain method and
- frequency domain method.
A. spatial domain method
The spatial domain method modifies or aggregates the pixels that make up the image, whereas the frequency domain method enhances the image linearly by positioning the invariant operator.
Some of the image enhancement techniques are:
a) Contrast Stretching b) Noise Filtering c) Histogram Modification
B. frequency domain method.
converts the signal from the time domain to the frequency domain by a fast Fourier transform (FFT), while the time domain (TD) method calculates peak-to-peak value of the pulse waveform directly from the time samples.
4. Image Restoration
Image restoration is the process of recovering a degraded or corrupted image by removing noise or blur to improve the image’s appearance. The degraded image is the result of the convolution of the original image, the degraded function, and additive noise.
The image is restored using prior knowledge of the noise or disturbance that is causing the degradation in the image. It is possible to perform it in two domains: spatial domain and frequency domain. T
he filtering action for restoring images in the spatial domain is performed directly on the operating pixels of the digital image, whereas the filtering action in the frequency domain is performed by mapping the spatial domain into the frequency domain via the fourier transform.
Following the filtering, the image is remapped into the spatial domain using the inverse fourier transform to obtain the restored image.
5 Morphological Processing
Morphological processing is a set of linear operations used to extract image components that can be used to represent and describe shape. A structuring element is a small set used to probe an image under investigation.
By positioning it in all possible locations in the image, the structural element is compared to the corresponding neighborhood of pixels. Some operations determine whether an element fits within the neighborhood, whereas others determine whether it hits or intersects the neighborhood.
Dilation, erosion, and their combinations are the fundamental morphological operations. Erosion is useful for removing structures of a specific shape and size provided by the structuring element, whereas dilation is useful for filling holes of a specific size and shape provided by the structuring element.
6. Image Segmentation
Image The process of partitioning a digital image into multiple segments in order to simplify and/or change the representation of an image into something more meaningful and easier to analyze is known as segmentation.
It could employ statistical classification, thresholding, edge detection, region detection, or any combination of these techniques.
As a result of the segmentation step, a set of classified elements is typically obtained. Techniques for segmentation can be classified as region-based or edge-based.
The edge-based techniques rely on image value discontinuities between distinct regions, and the segmentation algorithm’s goal is to accurately demarcate the boundary separating these regions.
7 Object Recognition
The process of assigning a label to an object in a digital image or video based on its descriptors is known as object recognition. eg:-vehicle. Object recognition is the process of teaching a computer to recognize a specific object from various perspectives, lighting conditions, and backgrounds.
The appearance of an object can change due to scene clutter, photometric effects, changes in shape, and viewpoints.