Bildverbesserungsalgorithmen

Bildverbesserung, commonly translated as image enhancement, is a field within image processing focused on increasing the perceptual quality of digital images. The objective is to make details clearer, reduce noise and blur, correct color and exposure, and generally render images more faithful to the captured scene or more suitable for downstream tasks. Enhancement does not introduce new scene content beyond what is supported by the data, though it may emphasize textures or adjust tonal relationships for readability.

Techniques span traditional signal processing and modern machine learning. Classic methods include denoising (for example non-local means or BM3D), deblurring (Wiener or Richardson–Lucy), and contrast or color correction such as histogram equalization. Sharpening methods, such as unsharp masking, accentuate edges. In recent years, deep learning approaches have become prominent, employing neural networks to perform super-resolution, denoising, or artifact removal, often achieving results that resemble higher-resolution imagery.

Applications appear across photography, film restoration, medical and satellite imaging, surveillance, and consumer apps that automate photo improvement. Limitations include the risk of introducing artificial details or artifacts, loss of original texture, and concerns about image authenticity, especially in journalism or forensic contexts. Effective evaluation remains challenging, mixing objective metrics with perceptual human judgments.

Common tools range from commercial software such as Photoshop and GIMP to open-source libraries like OpenCV and scikit-image. Ongoing research explores no-reference quality assessment, perceptual metrics aligned with human vision, and principled ways to balance enhancement with content integrity and ethical considerations in AI-based methods.