滤除椒盐噪声的开关核回归拟合算法

Switching kernel regression fitting algorithm for salt-and-pepper noise removal

针对椒盐噪声的去噪和细节保护问题,提出一种基于核回归拟合的开关去噪算法。首先,通过高效脉冲检测器对图像中的椒盐噪声像素点进行精确检测;其次,将所检测到的噪声像素点当作缺失数据,应用核回归方法对以噪声像素点为中心的邻域内的非噪声像素点进行拟合,得到符合图像局部结构特征的核回归拟合曲面;最后,以噪声像素点的空间坐标对核回归拟合曲面进行重采样,获得噪声像素点恢复后的灰度值,从而实现椒盐噪声的滤除。与经典的中值滤波器(SMF)、自适应中值滤波器(AMF)、改进型的方向加权中值滤波器(MDWMF)、快速开关中均值滤波器(FSMMF)、图像修补(II)等算法进行不同噪声密度的实验对比,所提算法的去噪结果图像的主观视觉质量均为最优;在低密度、中等密度以及高密度噪声场景下,所提算法对不同测试图像去噪结果的峰值信噪比(PSNR)分别平均提高了6.02 d B、6.33 d B和5.58 d B,且平均绝对误差(MAE)分别平均降低了0.90、5.84和25.29。实验结果表明,所提算法不仅能够有效去除各种密度的椒盐噪声,同时具备良好的图像细节保护性能。

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 6.02 dB, 6.33 dB and 5.58 dB, respectively; and the average Mean Absolute Error （MAE） was decreased by O. 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.