An Optimum Adaptive Parameterized Mask NHA Based Image Denoising

In image processing, the most significant challenges have been addressed due to the image denoising. Since, the segmentation of the original image components from the noisy image has high complexity. Therefore, the different image segmentation techniques were developed for effective segmentation by categorizing the noisy images. Among different methods, the image denoising including with the edge-preserving and segmentation according to the Mask Non-Harmonic Analysis (NHA) was the most utilized. In which, the image denoising was achieved in spatial domain by edge-preserving with fuzzy boundaries. In addition, the edge segmentation was performed by using the different segmentation parameters. However, the determination of parameter values was difficult for different noisy images. Moreover, the pre-determined threshold value was applied for the edge detection. Thresholding approach was applied for suppressing the unwanted information. However, the threshold value selection was required different values with respect to the different noise intensity images. Hence, in this paper, the machine learning algorithm Support Vector Machine (SVM) is proposed for determining the segmentation parameters automatically. The proposed algorithm is utilized for learning the segmentation parameters and output accuracy according to the noise levels and image frequency ranges. In addition, the edge detection is achieved by selecting the different threshold values based on the firefly optimization algorithm. Finally, the experimental results prove that the proposed Optimum Adaptive parameterized Mask NHA (OAMNHA) based image denoising has better performance than the other state-of-arts techniques. Keywords—Image denoising, Image segmentation, Edge-preserving, Mask NHA, Edge detection, SVM, Firefly algorithm