IMAGE DENOISING USING LU DECOMPOSITION AND FEATURE EXTRACTION USING GLCM
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Abstract
This paper proposes the removal of noise using LU decomposition and feature extraction using Gray level co-occurrence matrix (GLCM). The size of the image is reduced after decomposition and the compression ratio is calculated. When the compression ratio is less, the noise present in the data is also less. The image data contains contains redundant information and therefore it is necessary to decompose the data. There are many image decomposition techniques and they are spatial domain and frequency domain. Spatial domain operates the image on gray scale values. Texture is an important feature of an image. GLCM is used to obtain the second order statistical features for an image and it operates on spatial domain. The features of an image include color, texture, shape or domain specific features. . The texture features such as energy, entropy, homogeneity, correlation and contrast have been calculated. The aim of the paper is to extract the texture features of an image and to compare the size of an image before and after decomposition. The compression ratio is calculated and the performance of an image is evaluated before and after LU decomposition .
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