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 6.02dB, 6.33dB and 5.58dB, respectively; and the average Mean Absolute Error (MAE) was decreased by 0.90, 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.

Concerning highly accurate sub-pixel motion vector estimation, an accurate motion estimation algorithm based on upsampled phase correlation with kernel regression refining was proposed. Firstly, an upsampled phase correlation was computed efficiently by means of matrix-multiply discrete Fourier transform, and the initial estimation of motion vector with sub-pixel accuracy was achieved by simply locating its peak. Secondly, a kernel regression function was fit to the upsampled phase correlation values in a neighborhood of initial estimation. Finally, the initial estimation was refined with the location of peak found in the kernel regression fitting function, so as to obtain accurate estimation at arbitrary-precision. In the comparison experiments with some state-of-the-art algorithms such as Quadratic function Fitting (QuadFit), Linear Fitting (LinFit), Sinc Fitting (SincFit), Local Center of Mass (LCM) and Upsampling in the frequency domain (Upsamp), the proposed scheme achieved the average estimation error at 0.0070 in the case of noise-free, and increased the accuracy of motion estimation by more than 64%; while under the noise condition, the average estimation error of the proposed shceme was 0.0204, and the accuracy of motion estimation was improved by more than 47%. Experimental results show that the proposed scheme can not only improve the accuracy of motion estimation significantly, but also achieve good robustness to the influence of noise.