OVERLAPPING FIELDS WITH IMAGE PROCESSING
According to block 1,if input is an image and we get out image as a output, then it is termed as Digital Image Processing.
According to block 2,if input is an image and we get some kind of information or description as a output, then it is termed as Computer Vision.
According to block 3,if input is some description or code and we get image as an output, then it is termed as Computer Graphics.
According to block 4,if input is description or some keywords or some code and we get description or some keywords as a output,then it is termed as Artificial Intelligence
Advantages of Digital Image Processing:
- Improved image quality: Digital image processing algorithms can improve the visual quality of images, making them clearer, sharper, and more informative.
- Automated image-based tasks: Digital image processing can automate many image-based tasks, such as object recognition, pattern detection, and measurement.
- Increased efficiency: Digital image processing algorithms can process images much faster than humans, making it possible to analyze large amounts of data in a short amount of time.
- Increased accuracy: Digital image processing algorithms can provide more accurate results than humans, especially for tasks that require precise measurements or quantitative analysis.
Disadvantages of Digital Image Processing:
- High computational cost: Some digital image processing algorithms are computationally intensive and require significant computational resources.
- Limited interpretability: Some digital image processing algorithms may produce results that are difficult for humans to interpret, especially for complex or sophisticated algorithms.
- Dependence on quality of input: The quality of the output of digital image processing algorithms is highly dependent on the quality of the input images. Poor quality input images can result in poor quality output.
- Limitations of algorithms: Digital image processing algorithms have limitations, such as the difficulty of recognizing objects in cluttered or poorly lit scenes, or the inability to recognize objects with significant deformations or occlusions.
- Dependence on good training data: The performance of many digital image processing algorithms is dependent on the quality of the training data used to develop the algorithms. Poor quality training data can result in poor performance of the algorit
Digital Image Processing Basics
Digital Image Processing means processing digital image by means of a digital computer. We can also say that it is a use of computer algorithms, in order to get enhanced image either to extract some useful information.
Digital image processing is the use of algorithms and mathematical models to process and analyze digital images. The goal of digital image processing is to enhance the quality of images, extract meaningful information from images, and automate image-based tasks.
The basic steps involved in digital image processing are:
- Image acquisition: This involves capturing an image using a digital camera or scanner, or importing an existing image into a computer.
- Image enhancement: This involves improving the visual quality of an image, such as increasing contrast, reducing noise, and removing artifacts.
- Image restoration: This involves removing degradation from an image, such as blurring, noise, and distortion.
- Image segmentation: This involves dividing an image into regions or segments, each of which corresponds to a specific object or feature in the image.
- Image representation and description: This involves representing an image in a way that can be analyzed and manipulated by a computer, and describing the features of an image in a compact and meaningful way.
- Image analysis: This involves using algorithms and mathematical models to extract information from an image, such as recognizing objects, detecting patterns, and quantifying features.
- Image synthesis and compression: This involves generating new images or compressing existing images to reduce storage and transmission requirements.
- Digital image processing is widely used in a variety of applications, including medical imaging, remote sensing, computer vision, and multimedia.