基于稀疏表示的两级图像去噪

Two-stage Image Denoising Based on Sparse Representations

在噪声较严重的情况下对图像进行恢复,至今仍是一个挑战。该文提出一种基于图像稀疏表示的去噪方法。在原子库训练中,引入基于相关系数的匹配准则和原子库裁剪方案,很好地处理了图像结构提取和人为噪声抑制之间的矛盾。实验结果表明,该方法在主客观去噪性能上较同类方法有了显著的提高,在噪声强度较大的情况下,获得了比当前先进方法更好的主客观图像恢复质量。

It remains a challenging task to restore the image which is contaminated with heavy noise. In this paper, an image denoising method is proposed based on sparse representations. In the dictionary training stage, a matching criterion based on correlation coefficient is introduced and a dictionary pruning scheme is proposed to tackle the conflicting issues of structure extraction and artifact suppression. Experimental results show that the proposed method achieves significant improvements over the previous sparse denoising methods and outperforms the state-of-the-art methods in terms of both objective and subjective quality at high noise level.