Abstract
In this paper, a new method is proposed for image denoising inspired by the bidimensional empirical mode decomposition algorithm and the diffusion-based filtering. In the bidimensional empirical mode decomposition technique, the noisy image is decomposed into its respective intrinsic mode functions and the high-frequency and the low-frequency noises are removed with the help of the proposed diffusion filtering method using its parameters such as connectivity, conductance function, gradient threshold and the number of iterations. The image is reconstructed with the help of these denoised intrinsic mode functions. Performance of the proposed technique is evaluated in terms of the peak signal-to-noise ratio, the structural similarity index, the mean square error and is then compared with the already existing techniques on the image denoising. From the experimentation and results obtained, it is clear that the bidimensional empirical mode decomposition method using the diffusion filtering algorithm is efficient for the denoising of the images both qualitatively and quantitatively.
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Kommuri, S.V.R., Singh, H., Kumar, A. et al. Bidimensional Empirical Mode Decomposition-Based Diffusion Filtering for Image Denoising. Circuits Syst Signal Process 39, 5127–5147 (2020). https://doi.org/10.1007/s00034-020-01404-y
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DOI: https://doi.org/10.1007/s00034-020-01404-y