A fast computational technique based on a novel tangent sigmoid anisotropic diffusion function for image-denoising

A crucial aspect of contemporary image processing systems is image denoising. The anisotropic diffusion function is a feature of the partial differential equation employed for the purpose of noise reduction and the preservation of image characteristics such as edges. A new tangent sigmoid diffusion coefficient and a new adaptive threshold parameter have been proposed in this work, which leads to faster convergence. In comparison to traditional anisotropic diffusion model techniques, the proposed technique performs admirably. As evidenced by the results, which demonstrate that the new anisotropic diffusion technique is not only capable of efficiently removing noise, but also of maintaining content in the denoised image. The performance of the proposed method is evaluated using various metrics, including peak signal-to-noise ratio, convergence rate, structural similarity index, time complexity, and space complexity. When comparing the proposed approach to previous methods, it is evident that the proposed method outperforms in various aspects. These include a higher convergence rate (− 0.1278), a greater peak signal-to-noise ratio (37.9827 dB), a higher structural similarity index (0.97432), a lower time complexity (5.72 s), and a smaller space complexity (15.6 KB).