关键词: 椒盐噪声, 图像去噪, 开关, 脉冲检测器, 核回归拟合

Abstract: Concerning salt-and-pepper noise removal and details protection, an image denoising algorithm based on switching kernel regression fitting was proposed. Firstly, the pixels corrupted by salt-and-pepper noises were identified exactly by efficient impulse detector. Secondly, the corrupted pixels were take as missing data, and then a kernel regression function was used to fit the non-noise pixels in a neighborhood of current noisy pixel, so as to obtain a kernel regression fitting surface that met local structure characteristics of the image. Finally, the noisy pixel was restored by resampling of the kernel regression fitting surface in terms of its spatial coordinates. In the comparison experiments at different noise densities with some state-of-the-art algorithms such as Standard Median Filter (SMF), Adaptive Median Filter (AMF), Modified Directional-Weighted-Median Filter (MDWMF), Fast Switching based Median-Mean Filter (FSMMF) and Image Inpainting (Ⅱ), the proposed scheme had better performance in subjective visual quality of restored image. At low, medium and high noise density levels, the average Peak Signal-to-Noise Ratio (PSNR) of different images by using the proposed scheme was increased by 6.02dB, 6.33dB and 5.58dB, respectively; and the average Mean Absolute Error (MAE) was decreased by 0.90, 5.84 and 25.29, respectively. Experimental results show that the proposed scheme outperforms all the compared techniques in removing salt-and-pepper noise and preserving details at various noise density levels.

Key words: salt-and-pepper noise, image denoising, switching, impulse detector, kernel regression fitting