Łukasiewicz ANN for local image processing

Title:

Łukasiewicz ANN for local image processing

Creator:

Majerová, Dana and Kukal, Jaromír

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:baee8a0c-7abb-45a9-a2a9-784b2e013659
uuid:baee8a0c-7abb-45a9-a2a9-784b2e013659

Subject:

Łukasiewicz algebra, square root, neural network, and local image processing

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

One of the useful areas of the 2D image processing is called de-noising. When both original (ideal) and noisy images are available, the quality of de-noising is measurable. Our paper is focused to local 2D image processing using Łukasiewicz algebra with the square root function. The basic operators and functions in given algebra are first defined and then analyzed. The first result of the fuzzy logic function (FLF) analysis is its decomposition and realization as Łukasiewicz network (ŁN) with three types of processing nodes. The second result of FLF decomposition is the decomposed Łukasiewicz network (DŁN) with dyadic preprocessing and two types of processing nodes. The decomposition chain, which begins with FLF and converts it to ŁN and then to DŁN, terminates as Łukasiewicz artificial neural network (\lann) with dyadic preprocessing and only one type of processing node. Then the ŁANN is able to learn its integer weights in the ANN style. We are able to realize a set of individual FLF filters as ŁANN. Their preprocessing strategies are based on the pixel neighborhood, sorted list, Walsh list, and L-estimates. The quality of de-noising can be improved via compromise filtering. Two types of compromise de-noising filters are also realizable as ŁANN. One of them is called constrained referential neural network (CRNN). The other one is called dyadic weight neural network (DWNN). The compromise filters operate with the values from the set of individual filters. Both CRNN and DWNN are able to increase the quality of image processing as demonstrated on the biomedical MR image. All the calculations are realized in the Matlab environment

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2005 Volume:15 | Number:6

Harvested from:

CDK

Metadata only:

false