Digital Image Processing (DIP) is a field of study that involves the manipulation of digital images using computer algorithms. It encompasses a wide range of techniques and methods for analyzing, enhancing, and interpreting digital images. Here are some fundamental concepts and aspects of Digital Image Processing:
1. Digital Images:
- Pixel: The smallest element in a digital image. Pixels represent the building blocks of an image, and each pixel has a specific color or intensity.
- Resolution: The number of pixels in each dimension of an image, often expressed as width x height (e.g., 1920×1080 for Full HD).
- Color Models: Representations of color in images, such as RGB (Red, Green, Blue) and Grayscale.
2. Image Acquisition:
- Image Sensors: Devices like cameras capture images by converting light into electrical signals. Different sensors exist, such as CCD (Charge-Coupled Device) and CMOS (Complementary Metal-Oxide-Semiconductor).
3. Image Representation:
- Intensity Transformation: Adjusting the brightness and contrast of an image.
- Histogram: A plot of the frequency distribution of pixel intensities in an image.
4. Spatial Domain Operations:
- Point Processing: Operations applied to individual pixels, like contrast adjustment and gamma correction.
- Neighborhood Processing: Operations considering the spatial relationship of pixels, such as blurring and sharpening filters.
5. Frequency Domain Operations:
- Fourier Transform: A mathematical tool used to analyze the frequency components of an image.
- Filtering in Frequency Domain: Techniques like low-pass and high-pass filtering for image enhancement.
6. Image Enhancement:
- Contrast Enhancement: Methods to improve the visual contrast of an image.
- Histogram Equalization: A technique to redistribute pixel intensities for better utilization of the dynamic range.
7. Image Restoration:
- Image Deblurring: Removing blur caused by factors like motion or defocus.
- Noise Reduction: Techniques for mitigating noise introduced during image acquisition.
8. Image Compression:
- Lossless Compression: Techniques that retain all image information (e.g., Run-Length Encoding).
- Lossy Compression: Techniques that discard some information to achieve higher compression (e.g., JPEG).
9. Image Segmentation:
- Clustering Algorithms: Techniques to partition an image into meaningful regions.
- Edge Detection: Identifying boundaries between regions in an image.
10. Object Recognition:
- Pattern Matching: Identifying specific objects or patterns in an image.
- Feature Extraction: Extracting distinctive features for object recognition.
11. Morphological Operations:
- Erosion and Dilation: Basic operations used for shape analysis and image processing.
12. Applications:
- Medical Imaging: Diagnosis and analysis of medical images.
- Remote Sensing: Interpretation of satellite and aerial images.
- Computer Vision: Extracting information from images for automated systems.
Digital Image Processing is a multidisciplinary field with applications in various domains, including medicine, industry, entertainment, and research. It continues to evolve with advancements in computer technology and plays a vital role in modern image-based applications.
The field of digital image processing refers to processing digital images by means of a digital computer. A digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are called picture elements, image elements, pels, and pixels. Pixel is the term used most widely to denote the elements of digital images.
What is Image?
An image may be defined as a two-dimensional function f(x,y), where x and y are spatial
(plane) coordinates, and the amplitude of f at any pair of coordinates (x,y) is called the intensity of the image at that point.
What is the gray level?
The term gray level is used often to refer to the intensity of monochrome images. Color images are formed by a combination of individual 2-D images
APPLICATIONS OF DIGITAL IMAGE PROCESSING
- Remote sensing via satellites and other spacecraft
- Image transmission and storage for business applications
- Medical processing
- RADAR (Radio Detection and Ranging)
- SONAR(Sound Navigation and Ranging)
- Acoustic image processing
- Robotics and automated inspection of industrial parts.
- Recognition and analysis of objects contained in images obtained from deep space-probe missions.
- Processing of chest X-rays
- Teleconferencing
Components of Image Processing System:
Image Sensors:with reference to sensing, two elements are required to acquire a digital image. The first is a physical device that is sensitive to the energy radiated by the object we wish to image and the second is specialized image-processing hardware
Specialize image processing hardware: It consists of the digitizer just mentioned, plus hardware that performs other primitive operations such as an arithmetic logic unit, which performs arithmetic such as addition and subtraction and logical operations in parallel on images.
Computer: It is a general-purpose computer and can range from a PC to a supercomputer depending on the application. In dedicated applications, sometimes specially designed computer are used to achieve the required level of performance
Software: It consists of specialized modules that perform specific tasks a well-designed package also includes the capability for the user to write code, as a minimum, utilizing the specialized module. More sophisticated software packages allow the integration of these modules
Mass storage: This capability is a must in image processing applications. An image of size 1024 x1024 pixels, in which the intensity of each pixel is an 8-bit quantity requires one Megabyte of storage space if the image is not compressed
Image display: Image displays in use today are mainly color TV monitors. These monitors are driven by the outputs of image and graphics display cards that are an integral part of the computer system
Hardcopy devices: The devices for recording image includes laser printers, film cameras, heat-sensitive devices inkjet units, and digital units such as optical and CD ROM disk. Films provide the highest possible resolution, but paper is the obvious medium of choice for written applications
Image acquisition: It could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves processing such scaling.
Image Enhancement: It is among the simplest and most appealing areas of digital image processing. The idea behind this is to bring out details that are obscured or simply to highlight certain features of interest in an image. Image enhancement is a very subjective area of image processing.
Image Restoration: It deals with improving the appearance of an image. It is an objective approach, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image processing. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” enhancement result
Color image processing: It is an area that is been gaining importance because of the use of digital images over the internet. Color image processing deals with color models and their implementation in image-processing applications
What is SAMPLING AND QUANTIZATION?
Digitalizing the coordinate values is called sampling. Digitalizing the amplitude values is called quantization
Spatial Correlation
Correlation is the process of moving a filter mask over the image and computing the sum of products at each location