Abstract
Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation L1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i) each channel in a color image is contaminated independently, and contaminative components are independent and identically distributed; (ii) in a natural image the gradients of different components of a pixel are similar to one another. This method divides components into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the components are divided into the corrupted and the noise-free components; if the image is corrupted by random-valued impulses, the components are divided into the corrupted, noise-free, and the possibly corrupted components. Components falling into different categories are processed differently. If a component is corrupted, modified total variation diffusion is applied; if it is possibly corrupted, scaled total variation diffusion is applied; otherwise, the component is left unchanged. Simulation results demonstrate its effectiveness.
Highlights
Noise reduction is a fundamental issue in image processing
The proposed method divides components into different categories based on different noise characteristics
Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by Peak Signal to Noise Ratio (PSNR)/Mean Structural Similarity Index Measure (MSSIM)/FSIM results as well as the visual quality of restored images
Summary
Noise reduction is a fundamental issue in image processing. In order to reduce noise, a great number of works have been presented over the past several decades. The overwhelming majority of these works cope with the gray-scale images. With the increase in use of color images, more and more works to reduce noise for color images are rapidly growing. Color images often are corrupted by various types of noise. Impulsive noise is only considered, containing salt-and-pepper noise and random-valued impulses where they present themselves as occurring isolated chromatic points. To yield better images from their noisy versions, a series of various methods have been proposed
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