Abstract
Aimed at the problem of poor noise reduction effect and parameter uncertainty of pulse-coupled neural network (PCNN), a hybrid image denoising method, using an adaptive PCNN that has been optimized by grey wolf optimization (GWO) and bidimensional empirical mode decomposition (BEMD), is presented. The BEMD is used to decompose the original image into multilayer image components. After a GWO is run to complete PCNN parameter optimization, an adaptive PCNN filter method is used to remediate the polluted noise points that correspond to the different image components, from which a reconstruction of the denoised image components can then be obtained. From an analysis of the image denoising results, the main advantages of the proposed method are as follows: (i) the method effectively solves the deficiencies that arise from the critical PCNN parameter determination issue; (ii) the method effectively overcomes the problem of high-intensity noise effects by providing a more targeted and efficient noise reduction process; (iii) when using this method, the noise points are isolated, and the original pixel points are restored well, which can lead to preservation of image detail information. When compared with traditional image denoising process algorithms, the proposed method can yield a better noise suppression effect, based on indicators including analysis of mutual information (MI), structural similarity (SSIM), the peak signal-to-noise ratio (PSNR) and the standard deviation (STD). The feasibility and applicability of the proposed denoising algorithm are also demonstrated experimentally.
Highlights
The recent boom in the development of digital technology and multimedia communications has seen digital image analysis methods for nonlinear and nonstationary data receive widespread attention
The effectiveness of the proposed pulse-coupled neural network (PCNN)-bidimensional empirical mode decomposition (BEMD) when combined with the grey wolf optimization (GWO) method can be verified experimentally
The best parameters for the PCNN can be obtained from GWO, from which noise separation can be obtained and a pre-denoising process can be completed; a median filter will be applied for noise reduction
Summary
The recent boom in the development of digital technology and multimedia communications has seen digital image analysis methods for nonlinear and nonstationary data receive widespread attention. As an effective nonlinear digital data analysis method, it is well capable of isolating noisy pixel points and eliminating high-intensity noise during image processing [8]. Analysis of whole image denoising processes shows that the optimized PCNN combined with filtering can produce a particular noise reduction effect. Committed to PCNN image noise reduction research, an adaptive PCNN method that is combined with the GWO and BEMD algorithms is developed, which has significant benefits for use in image denoising. BEMD will be applied to decompose the raw heavily polluted image into different components This step is significant, solving the problem of a one-time direct processing, which makes it difficult to reduce the high-intensity image noise thoroughly. In terms of parameter optimization efficiency and actual noise reduction effects, the proposed method applied to image denoising is superior. GWO and BEMD; Section 3 is the experimental results and analysis; And the last section provides conclusions and challenges for future research
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