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.