椒盐图像的方向加权均值滤波算法

椒盐噪声是造成图像污染的主要因素之一，椒盐去噪是图像去噪领域的研究热点。方向加权中值滤波算法计算噪声点滤波输出时存在一定的问题，比如，未排除近邻噪声点的干扰，对方向的估计不准确，对局部灰度特性刻画不完整等。为此，提出一种方向加权均值滤波算法。此算法先根据方向灰度差异和灰度极值判断检测噪声点，然后根据对局部窗口噪声强度的估计自适应地选择递归或非递归滤波窗口的加权灰度均值作为滤波输出。仿真实验结果表明，提出的算法与现有的两种方向加权中值滤波算法相比，PSNR普遍提高了2~3dB和5~6dB，噪声密度高时提高的幅度更加明显；速度提高了接近10倍和30倍。

Directional weighted mean filter for image with salt & pepper noise

Salt & pepper noise is one of the key factors causing image contamination. Its removal is a research hotspot in image denoising. Directional weighted median filters face some issues when seeking restored gray levels of noise pixels, such as non-exclusion of noise neighbors' disturbance, inaccurate estimation for direction and incomplete depiction of local gray level characteristic. To alleviate the issues, a directional weighted mean filter is proposed. The proposed algorithm first detects noise pixels by using directional gray level differences and extreme judgment, then adaptively takes the weighted gray level mean of recursive or non-recursive filtering window as restored gray levels of noise pixels according to noise density estimation of local window. Simulation results show that, as compared with two existing directional weighted median filters, PSNR values of the proposed algorithm is usually increased by 23 dB and 56 dB, with a more obvious increment for high noise density. In addition, running speed is increased nearly 10 and 30 times.