This paper highlights the memristor bridge-based lowpass filter (LPF) and improved image processing algorithms along with a novel adaptive Gaussian filter for denoising image and a new Gaussian pyramid for scale invariant feature transform (SIFT). First, a novel kind of LPF based on the memristor bridge is designed, whose cutoff frequency and other traits are demonstrated to change with different time and memristance. In light of the changeable parameter of the memristor bridge-based LPF, a new adaptive Gaussian filter and an improved SIFT algorithm are presented. Finally, experiment results show that the peak signalto-noise ratio (PSNR) of our denoising is bettered more than 2.77 dB compared to the corresponding of the traditional Gaussian filter, and our improved SIFT performances including the number of matched feature points and the percent of correct matches are higher than the traditional SIFT, which verifies feasibility and effectiveness of our algorithm.
This paper proposes a mem-computing model of memristive network-based genetic algorithm (MNGA) by building up the relationship between the memristive network (MN) and the genetic algorithm (GA), and a new edge detection algorithm where image pixels are defined as individuals of population. First, the computing model of MNGA is designed to perform mem-computing, which brings new possibility of the hardware implementation of GA. Secondly, MNGA-based edge detection integrating image filter and GA operator deployed by MN is proposed. Finally, simulation results demonstrate that the figure of merit (FoM) of our model is better than the latest memristor-based swarm intelligence. In summary, a new way is found to build proper matching of memristor to GA and aid image edge detection.
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