Abstract
Impulsive noise removal usually employs median filtering, switching median filtering, 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 new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. 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 PSNR/SSIM results as well as the visual quality of restored images.
Highlights
Images often contain impulsive noise, which may arise from transmission errors, malfunctioning camera photo sensors, optic imperfections, or poor illumination
Experiments show that, for salt-andpepper noise model, denoising results obtained by the proposed method significantly outperform those obtained by the MED filter [1] and the common Switching Median Filter (SMF) [6]; the proposed method outperforms the results obtained by the Spectral Gradient Method (SGM) [7]
In the random valued impulse model, denoising results obtained by the proposed method significantly outperform the traditional total variation diffusion (TVD) and the New Selective Degenerate Diffusion (NSDD) [23] method
Summary
Images often contain impulsive noise, which may arise from transmission errors, malfunctioning camera photo sensors, optic imperfections, or poor illumination. The sources of image disturbances include lightning, strong electromagnetic interferences caused by faulty high voltage powerline insulation, car starters, and unprotected electric switches. These noise sources generate short high-energy pulses. As a result, corrupted images contain sparsely occurring isolated pixels, for example, white and black points, and grayscale points that differ significantly from their neighbors. Reducing such noise is critical in many applications, such as pattern recognition, image identification, and image fusion. Various methods have been proposed over the past several decades to produce clean images from noisy versions
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