👉 Camera mathematics is a fundamental aspect of digital imaging, bridging the gap between the physical world and digital representation. At its core, it involves understanding how light interacts with a scene and how this interaction is captured by a camera's sensor. The process begins with the lens, which focuses light onto the sensor, typically a CMOS or CCD array. Each pixel on the sensor corresponds to a small area of the scene, and its output represents the intensity of light (and thus color) at that point. The camera's math then translates this raw data into a digital image through several key steps: analog-to-digital conversion, where the continuous light intensity is converted into discrete digital values; demosaicing, which interpolates missing color information from adjacent pixels to create a full-color image; and ultimately, applying various algorithms for noise reduction, sharpening, and color correction to enhance the image quality. Additionally, mathematical concepts like Fourier transforms are used in de-noising and image reconstruction, while geometric transformations account for perspective distortions to ensure accurate representation of the scene. This intricate interplay of optics, electronics, and mathematics enables cameras to capture high-quality images and videos.
