Digital Image Processing Fundamentals: Sensors, Color, and Analysis
Introduction to Digital Image Processing
This document covers fundamental concepts in digital image processing, including sensor technology, color models, data representation, and essential image manipulation techniques like normalization, histogram processing, and binarization.
Principle of Operation of the CMOS Sensor
The CMOS (Complementary Metal-Oxide-Semiconductor) sensor works on a similar principle to the CCD. Light falls onto a silicon crystal, forming pixels and generating electrical charges within them. Each pixel has its own converter and its own ‘address’. This technology was created with the aim of producing integrated circuits. Its advantage is a relatively simple and cheap method of production.
Principle of Operation of the CCD Sensor
The CCD (Charge-Coupled Device) sensor is a detector that captures and registers the light that strikes it in the form of photons. A photon transmits its energy when it falls on the CCD, causing the release of internal electrons due to the photoelectric effect. The longer the exposure, the more electrons accumulate. Subsequently, an analog-to-digital converter processes the received signal to form data comprehensible to the computer.
Technological Differences: CCD vs. CMOS Sensors
Understanding the differences between CCD and CMOS sensors is crucial for selecting the appropriate technology for various imaging applications:
CCD Sensor Characteristics | CMOS Sensor Characteristics |
The content of a single pixel cannot be read independently. The entire matrix must be read, making this operation slower. | Any number of pixels can be read in any order. They operate much faster. |
The matrix has one charge-to-voltage transducer and one Analog-to-Digital (A/D) converter for the entire chip. | Each pixel of the CMOS sensor has its own charge-to-voltage transducer and a reading system for pixel content. In advanced CMOS sensors, each pixel has its own A/D converter. |
CCDs consume more power during operation, leading to faster heating and quicker battery exhaustion. | Consume less electrical power. |
Higher fill factor (the percentage of the pixel area sensitive to light). | Lower fill factor. |
Lower noise levels. | Higher noise levels. |
Acceptable dark current. | Larger dark current. |
Uses a single amplifier. | Since each pixel has its own amplifier, maintaining quality consistency across the array can be challenging. |
Greater disruptions in data transmission due to the large distance data must travel. | Small disturbances in data transmission due to the short distance data travels (on-chip processing). |
Higher manufacturing costs. | Low production cost. |
Greater photosensitivity. | Less photosensitivity. |
Basic Models of Color Space
Color spaces define how colors are represented digitally. Common color spaces based on the RGB model include sRGB, Adobe RGB, ProPhoto RGB, scRGB, and CIE RGB.
RGB (Red, Green, Blue): These are the three primary additive colors. When displayed together at maximum intensity, they yield white light. Black is obtained by displaying none of the primary colors.
CMYK (Cyan, Magenta, Yellow, Black): This subtractive model is primarily used in printing. Compared to RGB, CMYK has a smaller color gamut and typically produces less vivid colors with lower saturation.
HSB (Hue, Saturation, Brightness): Also known as HSV (Hue, Saturation, Value), this is an alternative model to RGB, often considered more intuitive and used widely in graphic programs like Adobe products. It is much easier to adjust the color by changing its brightness or saturation.
Pantone: This is a color standardization system that produces physical color patterns (swatches). Colors are marked with specific numbers and are created by mixing 18 base pigments.
HEX (Hexadecimal Notation): Colors described using a specific hexadecimal number format. It is commonly used for creating websites but is also frequently adopted by graphic designers due to its convenience.
Data Structures for Raster Images in MATLAB
A raster image is described by a grid (matrix, array, or map) of pixels. For example, a photograph from a digital camera is an image in raster form. In environments like MATLAB, you can map image data represented as a matrix where each row-and-column element corresponds to a rectangular patch of a specific geographic area, often implying topological connectivity to adjacent patches.
Methods of Digital Image Normalization
Image normalization consists of reducing or adjusting the range of changes in gray levels or color intensities of pixels. It is a process that changes the range of pixel intensity values.
The purpose of dynamic range expansion in various applications is usually to bring the image, or other types of signals, into a range that is more familiar or ‘normal’ to human senses. Often, the motivation is to achieve consistency in dynamic range for a set of data, signals, or images to avoid mental distraction or fatigue. For instance, a newspaper will strive to ensure all images in an issue share a similar range of grayscale values.
The Concept of the Image Data Histogram
An image data histogram is a function that assigns to each level of grayness or color intensity the number of pixels possessing that specific brightness level. The histogram can be represented as a graph where the horizontal axis displays consecutive possible levels of grayness or color intensity, and the vertical axis represents the count of pixels.
Equalization of the Image Histogram
Histogram equalization, or histogram flattening, involves the conversion of gray level or color intensity values of the pixels so that the number of pixels in each intensity interval is approximately the same. The objective of this technique is to give a linear trend to the cumulative probability function associated with the image. Histogram equalization significantly improves the global contrast of the processed image.
Digital Image Binarization: Concepts and Types
One of the basic methods of point processing of images is binarization. The binarization process involves converting a source image having many gray levels or color intensities into a resulting binary image, where pixels have only two values. Usually, these values correspond to black (0, zero brightness) and white (1, maximum brightness).
Binarization is a transformation often immediately preceding image analysis. Many measurements and complex transformations can only be performed effectively on binary images.
Types of Binarization
- Binarization with a lower threshold
- Binarization with an upper threshold
- Double-threshold binarization
- Multi-threshold binarization