基于噪声水平估计的图像盲去噪

摘要

Block-matching and 3D filtering ( BM3D) algorithm is one of the best image denoising algorithms. However, the application of the algorithm is constrained owing to high time complexity and the requirement of exact image noise level parameter. Thus, a fast block-matching and 3D filtering (FBM3D) algorithm is proposed, which uses a grid-based block-matching strategy. Then, the image noise is refined by iteration in which the starting point is set by SVM learning and the ending point is decided by image quality. The experimental results show that the proposed algorithm has a significant improvement in computation efficiency, visual effects and quantifiable performance evaluation.%三维块匹配(BM3D)去噪是当前去噪性能最好的算法之一。但由于时间复杂度较高，而且需要输入精确的图像噪声水平参数，极大地限制该算法的广泛应用。因此，文中首先采用基于网格的块匹配策略，提出快速三维块匹配(FBM3D)算法。然后提出基于迭代的盲图像噪声水平估计算法，由SVM学习算法确定迭代的初始值，再由图像质量判定迭代是否终止。测试实验表明，与原始的BM3D算法相比，该算法在计算效率、视觉感知效果和定量评测方面均有明显改善。