BildverarbeitungsPipelines

Bildverarbeitung, or image processing, is a field at the intersection of computer science and electrical engineering that focuses on the acquisition, manipulation, and analysis of digital images through algorithmic means. It includes low-level tasks such as image enhancement and restoration, as well as higher-level processing like feature extraction and pattern recognition. Unlike general computer vision, which emphasizes interpretation and decision making, image processing often centers on improving image quality and quantifying visual information.

Techniques span the spatial domain, including linear and nonlinear filtering, edge detection (eg, Canny), and morphological operations; and the frequency domain, using transforms such as the Fourier transform or wavelets. Color and geometric processing, noise reduction, and image reconstruction are common steps in pipelines. Increasingly, learning-based methods, including deep neural networks, are used for complex tasks such as segmentation, denoising, and object recognition.

Applications appear across medicine (diagnostic imaging and image-guided therapy), industry (quality control and automated inspection), remote sensing, automotive and robotics (vision for autonomous systems), surveillance, and consumer photography. Practical work often combines preprocessing, feature extraction, and evaluation against ground truth, using metrics like PSNR, SSIM, or intersection-over-union for segmentation.

Historically, digital image processing matured from early pixel-wise operations in the 1960s–1980s to contemporary real-time systems leveraging GPUs and specialized hardware. Common software tools include OpenCV, MATLAB’s Image Processing Toolbox, and scikit-image. Standards such as DICOM apply in medical contexts. The field continues to evolve with advances in machine learning, sensor technology, and embedded vision.