Full-color image processing involves handling and manipulating images that consist of multiple color channels. These channels, typically Red, Green, and Blue (RGB), define the color of each pixel in the image. This method is essential in areas like photography, medical imaging, and computer vision, where accurate color representation and manipulation are crucial for analysis. In … Read more
Pseudocolor (or false color) image processing is a technique where colors are assigned to grayscale values in a monochrome image based on a specified criterion. Unlike true color images, where colors represent actual spectral information, pseudocolor assigns artificial colors to enhance the visual interpretation of grayscale images. This process is particularly useful in scientific and … Read more
A color model, also called a color space or color system, is designed to provide a standardized method for specifying colors. This is essential for ensuring consistency and accuracy across various platforms and applications. In essence, a color model defines a coordinate system and a subspace within that system, where a unique point represents each … Read more
The human brain’s perception and interpretation of color is a complex physiopsychological process that is not fully understood. However, the physical nature of color can be described scientifically, supported by both experimental findings and theoretical principles. Sir Isaac Newton, in 1666, discovered that when sunlight passes through a glass prism, it separates into a continuous … Read more
In the field of medical imaging, the process of reconstructing images from projections plays a crucial role, particularly in technologies such as CT scans. This process, often referred to as back-projection, involves the transformation of 1D absorption data into 2D images. The figures provided illustrate the principles behind back-projection, starting with a simple object (like … Read more
Image processing involves various techniques to enhance or restore images. One fundamental technique used in noise reduction is the geometric mean filter. It is especially effective for removing certain types of noise while maintaining edge sharpness and avoiding excessive blurring. The geometric mean filter is based on the mathematical concept of the geometric mean, which … Read more
Image restoration is a critical process in digital image processing, aimed at recovering the original image from a degraded version. One of the advanced methods used for this purpose is Constrained Least Squares Filtering (CLSF). Unlike the Wiener filter, CLSF incorporates additional constraints that allow for improved restoration under certain conditions, especially when the power … Read more
The Wiener Filter is a classical approach in image restoration, aiming to reduce the degradation caused by factors such as noise and blurring in an image. The method is grounded in the assumption that both the image and the noise are random variables, and the objective is to estimate an uncorrupted image f^\hat{f}f^ such that … Read more
Image restoration is a significant aspect of image processing, aiming to recover an original image that has been degraded or corrupted by factors like noise, blurring, or distortion. Among various techniques used for image restoration, inverse filtering is one of the most straightforward and commonly used methods, especially for removing the effects of blurring. This … Read more
When attempting to restore an image that has been degraded, it’s critical to estimate the degradation function that caused the image’s decline in quality. There are three main methods for estimating this degradation function: observation, experimentation, and mathematical modeling. These methods form the foundation of image restoration techniques, often involving a process called blind deconvolution, … Read more
