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Multifractal theory has been widely used in different kinds of fields. In this paper, methods were proposed to extract two kinds of multifractal descriptors of gray series and two-dimensional surfaces for gray image based on the multifractal detrended fluctuation analysis. The proposed multifractal parameters can be well described by texture feature through the test of some textures. Three aspects of experiments have been conducted to verify the robustness of the proposed parameters, which include noise immunity, degree of image blurring and compression ratio. Comparisons were conducted between the proposed parameters and other kinds of texture feature parameters calculated by the standard multifractal analysis, the method of differential box counting and the methods of gray level co-occurrence matrix. Results demonstrate that the proposed exponents of H(2) and h(2) have great noise immunity and are robust to image compression and blurring.

Identification of affine deformed and simultaneously blur degraded images is an important task in pattern analysis. In this paper, we introduce an image normalization approach to derive blur and affine combined moment invariants (BACIs). In our scheme, the lowest order blur invariant moments are used as the normalization constraints and an appropriate normalization procedure is designed to guarantee that the constraints used in each step should not be affected in the subsequent normalization steps. A neural network (NN) model is then employed to classify the degraded images using the proposed BACIs. Experimental results show that the system has high classification accuracy.

Image de-noising is an essential tool for removing unwanted signals from an image. In Computed Tomography (CT) images, the image quality is degraded by the absorption of X-rays and quantum noise, which is generated due to the excitement of X-ray photons. Removal of noise and preservation of information in the CT images becomes a challenge for an imaging algorithm design. During the algorithm design selection of dataset is an important aspect for deducing results. The dataset used in this research comprises of 60 CT scan images of liver cancer archived from the arterial contrast enhanced phase. In this phase the cancer cells appear more intense as compared to the healthy liver tissue due to the absorption of contrast enhancing reagent. The experimentation for appropriate noise removal filter selection is done by testing the images using Mean, Median and Weiner Filters. The filter selected should give an image output which has minimal randomness, sharper boundaries and no blur. The de-noised image will provide a better visibility of the disease to the radiologist and physician. The performance parameters used for the assessment of various filters used in the study include visual assessment, entropy and signal to noise ratio (SNR) of the images. Median filter gives an accuracy of 96%, mean filter is 76.2% accurate with respect to original information and Weiner filters has an accuracy of 79.7%.