In the arena of digital image processing, mixed noise is normally shown. Nevertheless, the main limitation of the existing noise removal methods is the exploitation of the statistics of the original contaminated image only. This paper proposes a rapid and high accurate mixed noise removal method by combining Pulse Coupled Neural Networks (PCNN) and regularization of Perona-Malik equation (P-M equation) in order to remove unwanted contamination. In this regard, the locations of impulse noise are positioned by using PCNN, the second-generation wavelet filter is used in order to suppress the mixed noise into small local neighborhood, and then the full noisy image is denoised by exploiting the regularization of P-M equation. The fine details and sharp edges are well preserved in the proposed method. In addition, subjective and objective analyses are showed that the visual quality of the denoised technique outperformed state of the art noise removal methods. The experimental evaluation is conducted on well-known benchmark database images with mixed noise type. Furthermore, the results show that our proposed method can obtain more accurate and more reliable noise removal images than other approaches. Experimental results show that the proposed method provides accurate denoising performance with a low computational cost compared to nonlocal processing like NL-mean method.