Digital image processing

Digital image processing is the study of algorithms applied to digital images. Typical problems covered by this field include

• geometric transformations such as enlargement, reduction, and rotation;
• Color corrections such as brightness and contrast adjustments, quantization, or conversion to a different color space;
• Combination of two or more images, e.g. into an average, blend, difference, or image composite .
• Interpolation, demosaicing, and recovery of a full image from a mosaic image (e.g. a Bayer pattern, etc.);
• Noise reduction and other types of filtering, and signal averaging;
• Edge detection and other local operators;
• Segmentation of the image into regions;
• image editing and digital retouching;
• Extending dynamic range by combining differently exposed images (generalized signal averaging of Wyckoff sets ).

and many more.

Besides static two-dimensional images, the field also covers the processing of three-dimensional signals such as digital video and the output of tomographic equipment. Some techniques, such as morphological image processing, are specific to binary or grayscale images.

The name 'image processing' is most appropriate when both inputs and outputs are images. The extraction of arbitrary information from images is the domain of image analysis, which includes pattern recognition when the patterns to be identified are in images. In computer vision one seeks to extract more abstract information, such as the 3D description of a scene from video footage of it. The tools and concepts of image processing are also relevant to image synthesis from more abstract models, which is a major branch of computer graphics.

Some applications are:

• Digital photography and printing
• Satellite image processing
• Medical image processing
• Face detection, feature detection, face identification
• Microscope image processing

Some related concepts are:

• Classification
• Feature extraction
• Pattern recognition
• Projection
• Multi-scale signal analysis
• Principal components analysis
• Independent components analysis
• Self organizing map
• Hidden Markov model
• Neural networks

See also

• Computer vision