Noise filtering method for color images based on LDA and nonlinear diffusion
Woong Hee Kim, Thomas Sikora
Abstract
Woong Hee Kim, Thomas Sikora
Abstract
The purpose of noise filtering for images is to preserve features such as edge or corners in images, while reducing noise. Recent noise filtering algorithms based on diffusion equation shows the satisfactory results to some extent, if the noise is additive Gaussian noise. However, if the noise is not additive Gaussian noise, the filtering result is not satisfactory. In this paper, we propose a noise filtering method for color images based on LDA and nonlinear diffusion, which makes use of a common diffusion control. Experimental results with images degraded by additive Gaussian noise, salt and pepper noise, and multiplicative noise are presented.
OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The purpose of noise filtering for images is to preserve features such as edge or corners in images, while reducing noise. Recent noise filtering algorithms based on diffusion equation shows the satisfactory results to some extent, if the noise is additive Gaussian noise. However, if the noise is not additive Gaussian noise, the filtering result is not satisfactory. In this paper, we propose a noise filtering method for color images based on LDA and nonlinear diffusion, which makes use of a common diffusion control. Experimental results with images degraded by additive Gaussian noise, salt and pepper noise, and multiplicative noise are presented.
Key concepts: Salt-and-pepper noise, Value noise, Gaussian noise, Gradient noise, Noise (video), Multiplicative noise, Median filter, Noise measurement