a filtering zashumlennyh images at a preliminary stage of machining spektrozonalnyh images

The filtering spektrozonalnogo images means use of those threshold functions which make zeroing of vejvlet-factors in a neighbourhood of zero [118,119].

Coarse threshold cutting off of factors keeps without changes all koeffitsientyurovnjabolshie or equal to threshold Г-1, and

Turns other factors, not fulfilling to a condition presented more low, into a zero:

Coarse threshold cutting off of factors acts as a zero zone in known vejvlet-codecs of type SPIHT (from English Set Partitioning in Hierarchical Trees) and JPEG2000 (from English Joint Photographic Experts Group).

There is such deficiency, that at conservation detailing vejvlet - factors noise remain. Also at signal restoration there are parasitic harmonics. Both deficiencies can be removed thanks to soft threshold machining.

Two aspects of threshold cutting off of vejvlet-factors mentioned above are the most used in many problems of a filtering and compression of images.

Quality shumopodavlenija and compression of images depends not only on the chosen aspect of threshold cutting off, but also and from a way of application.

There are 2 aspects of threshold machining: local machining and global machining. Local threshold machining is applied in a case when for all levels of decomposition the fixed value of a threshold g-4 is used, Global machining is gained in case of the consecutive

112 applications of any function of threshold machining at all levels of decomposition.

At noise act one part of a signal can appear coherent structure at one basis, and another - to have strong correlation with other basis.

Thus, it is possible to draw a leading-out what to reach high quality of restoration of the original image on one basis it is not obviously possible because of loss of an informative part of this image.

In [120] the algorithm of orthogonal persuit of the basis, carrying out search of a set of basises in the rests for a filtering (restoral) of music records is presented. The given algorithm can be used at the solution of a task in view of a filtering zashumlennogo images.

In-process [121] the algorithm of a filtering zashumlennyh images on the basis of vejvlet-package transformation which analyses the rests in substrips is offered and optimises structure of package decomposition on following algorithm.

If at machining of images it becomes clear, that noise is white stationary then the image can be written down as follows: h = f—??,

Where n - noise with zero average А7 [? їJ = 0, a matrix With = σi^Iи a dispersion

The algorithm is applied to a noise filtering adaptive vinerovskoj the filterings, based on statistical estimations of fragments of the image within a sliding window by dimensions of a quantity? i h pixels.

For all rules of a sliding window with the central pixel, which co-ordinates (r, c), are computed following characteristics:

- Luminance mean

- Dispersion

The total image is formed under the following formula:

Where v - power of white noise.

Use of expression (2.24) allows to improve a relationship a signal/noise which quantitative characteristics are presented in section


One more important problem of machining spektrozonalnyh images is abbreviation of computing losses of machining of difficult structured data. For the solution of this problem the matching algorithm optimising number significant vejvlet - factors is offered.


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A source: TITOV Dmitry Vitalevich. WORKING out And RESEARCH of METHODS, ALGORITHMS And HARDWARE COMPONENTS of MACHINING SPEKTROZONALNYH of IMAGES. The dissertation on competition of a scientific degree of a Dr.Sci.Tech. Kursk - 2018. 2018

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