A novel adaptive high-performance and nonlinear filtering algorithm (AHPNFA) is proposed for the removal of mixed noise from corrupted images. This algorithm contains two stages: noise determining and noise removing. In the first stage, for each filtering window of the processed image, the center pixel and its nearest neighbors are modeled as a statistical variable. By exploiting Chebyshev's theorem, the fuzzy mean process is used to estimate adaptively the detection parameters that are required in determining whether the current pixel is corrupted or not. In the second stage, the Radon transform is performed to determine the texture direction probability density distributions of local areas of images, then using the texture direction probability density distributions and the local features of the image remove the noisy pixels. To demonstrate the advantages, the proposed AHPNFA is compared with the other test filters both visually and quantitatively. Simulation results show that the proposed AHPNFA has better values in important evaluation metrics; in particular, the computational complexity of the AHPNFA is about five to 11 times lower than the other test filters used in the comparison, therefore, the proposed AHPNFA has better performances.