关键词: 彩色图像, 高斯加权, 非局部均值, 自适应流形, 高维滤波器, 图像降噪

Abstract: Using vector method for color image denoising, the complexity of the algorithm is high and cannot achieve real-time performance. A method for high fidelity color image denoising was proposed based on Gaussian weighting and adaptive manifold. Firstly, it used the non-local means algorithm to get high-dimensional data, and used the improved Gaussian kernel to calculate the weight of color image. Secondly, splatting method was used to deal with the high-dimensional data, and a Gaussian distance-weighted projection of the colors of all pixels was performed onto each adaptive manifold. Thirdly, smooth dimensionality reduction was done on convection shape, and iterative method was used for image smoothing. Finally, the final filter response was computed for each pixel by interpolating blurred values gathered from all adaptive manifolds. The experimental results show that the algorithm has a superior denoising performance than the original one, and it also can improve real-time performance. By using this algorithm, the details can be preserved well. Peak Signal-to-Noise Ratio (PSNR) can be improved nearly 2.0dB, and Structural Similarity Index Measurement (SSIM) can be improved more than 1%.

Key words: color image, Gaussian weighting, non-local means, adaptive manifold, high-dimensional filter, image denoising