Image processing

Image processing is the application of signal processing techniques to the domain of images — two-dimensional signals such as photographs or video.

Most of the signal processing concepts that apply to one-dimensional signals — such as resolution, dynamic range, bandwidth, filtering, etc. — extend naturally to images as well. However, image processing brings some new concepts — such as connectivity and rotational invariance — that are meaningful or useful only for two-dimensional signals. Also, certain one-dimensional concepts — such as differential operators, edge detection, and domain modulation — become substantially more complicated when extended to two dimensions.

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.

A few decades ago, image processing was done largely in the analog domain, chiefly by optical devices. Optical methods are inherently parallel, and for that reason they are still essential to holography and a few other applications. However, as computers keep getting faster, analog techniques are being increasingly replaced by digital image processing techniques — which are more versatile, reliable, accurate, and easier to implement.

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 time-varying signals such as video and the output of tomographic equipment. Some techniques, such as morphological image processing, are specific to binary or grayscale images.

Some applications are:

• 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 component analysis
• Self organizing map
• Hidden Markov model
• Neural networks

See also

• Digital image processing
• Computer graphics
• Computer vision
• GPGPU
• optics
• photography
• imaging
• digitizing
• super-resolution

Useful links

• Photo Editing Software
• Image Processing Algorithms
• Hypermedia Image Processing Reference
• EMVA - European Machine Vision Association
• Software for Image Processing and Machine Vision
• Computer Vision, an external wiki