PDEs-Based Method for Image Enhancement
Ehsan Nadernejad, Hamidreza Koohi, Hamid Hassanpour · 2008
Removing noise from data is often the first step in data analysis. De-noising technique should not be only reduce the noise, but do so without blurring or changing the location of the edges. Many approaches have been proposed to accomplish this; in this paper, we have compared three recently developed techniques for image enhancement and denoising. These methods are based on the use of partial differential equations, including second order, fourth order, and the complex partial differential. We consider various well-known measuring metrics used in image processing applied to standard images in this comparison. In this study, it is shown that the capability of the PDE-based approaches depends highly on the neighboring structure. Our investigations show that in an image where the energy of noise is low, the complex diffusion method offers a better result in image denoising compared to other methods. However, when the energy of the noise increases, performance of the complex diffusion method declines. In general, for the case when the energy of noise in an image is unpredictable, using the heat equation for image denoising is recommended.