- Image Acquisition
This is the foremost step of digital image processing. Image acquisition is the direct reprographic process of a physical scene / object in the digital form or it may be obtained from another image in an analog form as photographs, films etc.
- Image Restoration
This step deals with improving the appearance of an image. The purpose of image restoration is to reconstruct the images corrupted by noise. As the image restoration techniques are to be developed in accordance with the mathematical or probabilistic models of image degradation, they are highly objective.
- Image Enhancement
This process also aims at augmenting the quality of an image in terms of its contrast and / or brightness. The enhancement techniques subjectively modify the histogram of the input image as demanded by the application domains. For instance, an enhancement method developed for X-ray images need not be suitable for satellite images. Hence, the enhancement techniques are to be devised in accordance with the requirements and limitations of the application domains.
- Image Compression
As the name suggests, it deals with the techniques which reduce the storage requirement of an image in the order of bits per pixel. Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art or comics. Lossy methods are especially suitable for natural images such as photographs in applications where minor loss of fidelity is acceptable to achieve a substantial reduction in bit rate. However, lossy compression methods, especially when used at low bit rates, introduce compression artifacts.
- Morphological Processing
Morphological processing uses a collection of non-linear operations to manipulate the geometric shape or morphological features of an image. Morphological operations rely only on the relative ordering of pixel values, not on their numerical values. Hence, they are specially suited for processing the binary images.
- Image Segmentation
This element of image processing describes the procedures which partition an image into its constituent parts or objects. Both semiautomatic and automatic segmentation techniques find their applications in object segmentation for object recognition, image analysis etc.
- Image Representation
The output of segmentation is generally the regions of interest or the boundaries / contours of such regions. Boundary representation is required, when the focus of investigation is on the external shape characteristics of the objects / regions, whereas regions are targeted, when their internal properties are to be analysed.
- Image Description
This process otherwise known as feature selection refers to the extraction of attributes of an image, from which some quantitative information of interest can be obtained for differentiating one class of objects from another.
- Colour Image Processing
This area has been gaining in importance due to its increased usage in medical imaging, satellite imaging, multimedia etc., as the colour component of an image is also used as the vital parameter in processing.