CAJ | 학술논문

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。
삼유괴필배(BM3D)거조시당전거조성능최호적산법지일。단유우시간복잡도교고，이차수요수입정학적도상조성수평삼수，겁대지한제해산법적엄범응용。인차，문중수선채용기우망격적괴필배책략，제출쾌속삼유괴필배(FBM3D)산법。연후제출기우질대적맹도상조성수평고계산법，유SVM학습산법학정질대적초시치，재유도상질량판정질대시부종지。측시실험표명，여원시적BM3D산법상비，해산법재계산효솔、시각감지효과화정량평측방면균유명현개선。
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.