Image processing

In the broadest sense, image processing is any form of information processing for which both the input and output are images, such as photographs or frames of video. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it.

Solution Methods
A few decades ago, image processing was done largely in the analog domain, chiefly by optical devices. These optical methods are still essential to applications such as holography because they are inherently parallel; however, due to the significant increase in computer speed, these techniques are increasingly being replaced by digital image processing methods.

Digital image processing techniques are generally more versatile, reliable, and accurate; they have the additional benefit of being easier to implement than their analog counterparts. Specialized hardware is still used for digital image processing: computer architectures based on pipelining have been the most commercially successful. There are also many massively parallel architectures that have been developed for the purpose. Today, hardware solutions are commonly used in video processing systems. However, commercial image processing tasks are more commonly done by software running on conventional personal computers.

Commonly Used Signal Processing Techniques
Most of the signal processing concepts that apply to one-dimensional signals also extend to the two-dimensional image signal. Some of these one-dimensional signal processing concepts become significantly more complicated in two-dimensional. Image processing brings some new concepts, such as connectivity and rotational invariance, that are meaningful only for two-dimensional signals.

The fast fourier transform is often used for image processing operations because it reduces the amount of data and the necessary processing time.

One-Dimensional Techniques

 * Resolution
 * Dynamic range
 * Filtering
 * Differential operators
 * hellooo detection
 * \ modulation
 * Noise reduction
 * Noise reduction

Two-Dimensional Techniques

 * Connectivity
 * Rotational invariance

Typical Problems

 * Geometric transformations such as enlargement, reduction, and rotation
 * Color corrections such as brightness and contrast adjustments, quantization, or conversion to a different color space
 * Registration (or alignment) of two or more images
 * 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 RAW image format like a Bayer filter pattern
 * 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.

Applications

 * Photography and printing
 * Satellite image processing
 * Medical image processing
 * Face detection, feature detection, face identification
 * Microscope image processing
 * Car barrier detection

Related Concepts

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