Digital Image Processing

2786 Words6 Pages

1.1 GENERAL In Digital image processing manipulation of images by computer is a relatively modern growth of human’s ancient captivation with visual inspirations. In recent history, it has been used to reasonably every type of imagery, with varying degree of success. The essential subjective influence of pictorial displays attracts a disproportionate amount of concentration from scientist and end used. Image processing suffers from myths, misunderstanding, misconceptions, and misinformation. The main goal of image compression is to reduce the storage requirement for digital imaging and the time required for image transfer, but at the cost of compression and decompression time. Image restoration is the process of removing noise in an image and assessing the clean original image. Noise may occur in various forms such as motion blur, noise, and camera misfocus. Image segmentation is the process of partitioning a digital image into disjoint, connected sets of pixels, one of which corresponds to the background and the remainder to the objects in the image. Segmentation matching can be used to locate objects of known appearance in an image to search for specific pattern. Image processing is a vital supervision under which reduce diverse aspects of optics, electronics, mathematics, photography, and computer technology. Image processing is plagued with check and contradictory jargon taken from different fields. Digital image processing involves image acquisition, enhancement, restoration, compression, segmentation, representation and description. Image acquisition is first process in digital image Processing and can be broadly explained as the action of retrieving an image from some source, usually a hardware-based source. The aim of image... ... middle of paper ... ...IDWT are the horizontal, vertical, and diagonal images, only used Multiresolution representations are very effective for analyzing the information content of images. In study the properties of the operator which approximates a signal at a given resolution that the difference of information between the approximation of a signal at the resolutions 2’ + ’ and 2j can be extracted by decomposing this signal on a wavelet orthonormal basis of L*(R”). In LL(R ), a wavelet orthonormal basis is a family of functions, which is built by dilating and translating a unique function t+r (xl. This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror lilters. For images, the wavelet representation differentiates several spatial orientations [1].

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