imageprocessing

Image processing is the field that develops algorithms to manipulate and analyze digital images to improve quality, extract information, or automate tasks. It focuses on processing pixel data, often through systematic pipelines applied to acquired images.

A typical workflow includes image acquisition, preprocessing (denoising, geometric correction), enhancement (contrast, sharpness), restoration (deblurring), segmentation (region partitioning), feature extraction, and interpretation or recognition.

Techniques are categorized as spatial-domain methods, which modify pixel values directly, and frequency-domain methods, which operate on transformed representations. Common tools include filtering (smoothing, sharpening), morphological operations, and transforms such as the Fourier, discrete cosine, and wavelet transforms.

Key algorithms include edge detectors (Sobel, Canny), thresholding, clustering-based segmentation (k-means), and texture analysis. Image compression and color management are practical considerations for storage and display, including formats like JPEG and PNG.

Applications span medical imaging, remote sensing, industrial inspection, document analysis, photography, and computer-vision preprocessing. Quality is assessed with metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM); segmentation performance uses precision, recall, and the Jaccard index. Popular software tools include OpenCV, scikit-image, and MATLAB's Image Processing Toolbox.