bildbehandlingar

Bildbehandlingar, commonly translated as image processing, refers to the manipulation and analysis of digital images using computational methods. The field covers techniques that improve image quality, extract information, or prepare images for further analysis. The term is often used in Swedish to describe a broad set of low-level operations that act on pixel data, as opposed to higher-level interpretation in computer vision.

Image processing can be categorized into enhancement, restoration, reconstruction, compression, and analysis. Enhancement improves visibility without changing the scene (for example, contrast adjustment and denoising). Restoration aims to reverse degradation such as motion blur or noise. Compression reduces data size for storage or transmission. Analysis includes segmentation, feature extraction, and measurement.

Techniques include spatial-domain operations such as filtering and convolution; frequency-domain methods using Fourier transforms; edge detection (Sobel, Canny); histogram equalization; and thresholding. Color processing uses color spaces (RGB, HSV) and color balancing. Modern pipelines often combine supervised or unsupervised learning for tasks like segmentation or recognition.

Applications span several domains: digital photography and video, medical imaging (MRI, CT), remote sensing and satellite imagery, industrial inspection, surveillance, and document analysis (OCR). In research, image processing supports quantification, visualization, and preprocessing for computer vision tasks.

Typical workflow involves acquisition, preprocessing (noise reduction, normalization), registration or alignment, segmentation, and feature extraction or reconstruction. Software tools include OpenCV, MATLAB, and Python libraries such as scikit-image, Pillow, and NumPy. Standards and formats like JPEG, PNG, and TIFF are used for storage and interchange.

Ethical and legal considerations include privacy, consent for processing of biometric images, and potential biases in automated analysis. As a mature field, bildbehandlingar continues to integrate advances in machine learning, enabling more capable image interpretation while demanding careful validation.