Image processing aims to analyze and understand the content of an image to extract useful information for various applications.

The RGB color model (Red, Green, Blue) is widely used for representing images in digital form. It consists of three primary colors that can be combined in different proportions to create a wide range of colors.

Color correction is the process of adjusting the brightness, contrast, and color balance of an image to improve its visual appearance or to correct for color distortions.

Median filtering is a nonlinear image processing technique that replaces each pixel with the median value of its neighboring pixels. This helps to reduce noise and preserve edges, resulting in a clearer and sharper image.

Segmentation is the process of dividing an image into smaller, uniform regions called segments. This is often done to simplify the analysis of the image or to extract specific objects or features from it.

Edge detection is a technique used to detect the boundaries of objects in an image. It identifies points in the image where the intensity of the pixels changes significantly, indicating the presence of an edge.

Quantization is the process of reducing the number of colors in an image by mapping similar colors to a smaller set of representative colors. This is often done to reduce the file size of an image or to improve its performance on low-color displays.

Anti-aliasing is a technique used to create the illusion of a smooth gradient between two colors. It works by blending the colors together at the pixel level, reducing the appearance of jagged edges.

Color conversion is the process of converting an image from one color space to another. This is often done to match the color space of the display device or to facilitate further image processing operations.

Histogram equalization is a technique used to enhance the contrast between different regions of an image by adjusting the distribution of pixel values. This helps to bring out details and improve the overall visual appearance of the image.

Denoising is the process of removing unwanted noise from an image. This can be caused by various factors such as sensor noise, transmission errors, or environmental conditions. Denoising algorithms aim to preserve the important features of the image while reducing the noise.

Lossless compression is a technique used to reduce the size of an image without losing any significant detail. It works by removing redundant information from the image while preserving the original pixel values. This allows for significant file size reduction without compromising the image quality.

Noise addition is the process of adding artificial noise to an image. This can be done for various reasons, such as to improve the security of an image or to simulate the effects of noise in real-world scenarios.

Thresholding is a technique used to create a binary image from a grayscale image. It involves setting all pixel values below a certain threshold to black and all pixel values above the threshold to white.

Image stitching is the process of combining multiple images into a single composite image. This is often done to create panoramic images or to extend the field of view beyond the limitations of a single camera.