Abstract
The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.
Highlights
In this paper, a parallel array model of FHN neuron based on stochastic resonance (SR) is proposed for image restoration
In order to improve the performance of degraded images, it can be applied to FHN nonlinear parallel array, the process steps are as follows
The image is subjected to dimensionality reduction by row or column directional scanning, so that the original grayscale image is converted into a 1D signal of length H1×MN (M and N are the rows and columns of the original grayscale image). en the 1D image sequence H1×MN is encoded as an eight-bit binary sequence Q1×8MN consisting of 0 and 1 with a length of 8 × M × N
Summary
The general image degradation model caused by noise is described as follows: f(i, j) s(i, j) + ξ(i, j) for 1 ≤ i ≤ M, 1 ≤ j ≤ N, (1). In order to improve the performance of degraded images, it can be applied to FHN nonlinear parallel array, the process steps are as follows. E demodulated binary 1D signal Y1×8MN is decoded and Y1×8MN is inversely scanned by column or row to obtain a restored image OM×N. e above is the FHN array model and the processing of image restoring. PSNR is described as an objective criterion for evaluating image quality It is defined as follows: PSNR 10 × log10M25S5E22,.
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