This article introduces the fundamentals of image processing using OpenCV. It begins with a brief overview of three essential functions: reading an image, displaying it, and accessing pixel values. Following this, we discuss different types of image noise—specifically Gaussian noise and salt-and-pepper noise—and demonstrate how to add them to an image.
After introducing the noise, we apply two common filtering techniques—median filtering and averaging filtering—to reduce the noise and improve image quality. These filters are particularly useful in removing unwanted artifacts while preserving important details in the image.
In OpenCV, several key functions are used for basic image input, output, and display. The namedWindow() function creates a window that can be referenced by name, allowing for flexible resizing and aspect ratio control. The imshow() function is used to display an image in a window, while imread() reads an image file into a matrix (Mat) structure.
The Mat object is central to OpenCV operations. It consists of a header and the actual data. The header contains information such as size, type, and pointer to the data. The data itself is stored separately and managed through reference counting, which allows multiple Mat objects to share the same data without duplication.
To access pixel values in a Mat, you can use pointers, iterators, or the at() method. Each approach has its own advantages depending on the context and performance requirements.
Image noise refers to random variations in brightness or color that can degrade image quality. Noise can be classified based on its origin, relationship to the image signal, and probability distribution. Common types include Gaussian noise, which follows a normal distribution, and impulse noise, such as salt-and-pepper noise, which appears as random black and white pixels.
To simulate noise, we use C++11's random number generation facilities. For example, Gaussian noise is added by generating random values from a normal distribution and adding them to each pixel. Salt-and-pepper noise is created by randomly setting some pixels to either black or white.
Once noise is introduced, we apply filters to reduce its impact. Median filtering is effective at removing salt-and-pepper noise without blurring edges, while averaging (or mean) filtering is commonly used for Gaussian noise but may cause some blur in the image.
OpenCV provides built-in functions like blur() for averaging filtering and medianBlur() for median filtering. These functions take parameters such as the input image, output image, and kernel size. The choice of filter depends on the type of noise present in the image.
In summary, this article serves as an introduction to basic OpenCV operations, including image reading, noise addition, and filtering techniques. It demonstrates how to manipulate images programmatically and highlights the importance of choosing the right filtering method based on the type of noise encountered.
Plano-convex cylindrical lenses feature a single plano and single convex cylindrical surface, while their focal length is positive. These lenses operate only in one dimension rather than two, compared to plano-convex spherical lenses.
Plano convex cylindrical lenses are utilized to compress light in 1 axis. In order to generate a line image from a point of light and to change the aspect ratio of an image, a positive (convex) Cylindrical Lens is used. It is also applied to focus collimated input light to a line. Plano convex cylindrical lenses are normally used for line detector arrays, anamorphic beam shaping, laser projection, laser line focusing, and illumination of slit.
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