Image denoising plays an important role in image processing, which aims to separate clean images from noisy images. A number of methods have been presented to deal with this practical problem over the past several years. The best currently available wavelet-based denoising methods take advantage of the merits of the wavelet transform. Most of these methods, however, still have difficulties in defining the threshold parameter which can limit their capability. In this paper, we propose a novel wavelet denoising approach based on unsupervised learning model. The approach taken aims at exploiting the merits of the wavelet transform: sparsity, multi-resolution structure, and similarity with the human visual system, to adapt an unsupervised dictionary learning algorithm for creating a dictionary devoted to noise reduction. Using the K-Singular Value Decomposition (K-SVD) algorithm, we obtain an adaptive dictionary by learning over the wavelet decomposition of the noisy image. Experimental results on benchmark test images show that our proposed method achieves very competitive denoising performance and outperforms state-of-the-art denoising methods, especially in the peak signal to noise ratio (PSNR), the structural similarity (SSIM) index, and visual effects with different noise levels.

图像去噪在图像处理中起着重要作用，图像处理旨在将干净的图像与嘈杂的图像分开。在过去的几年中，已经提出了许多方法来解决这个实际问题。当前最好的基于小波的去噪方法利用了小波变换的优点。然而，大多数这些方法在定义阈值参数方面仍然有困难，这会限制它们的能力。在本文中，我们提出了一种基于无监督学习模型的小波去噪方法。采取的方法旨在利用小波变换的优点：稀疏性，多分辨率结构以及与人类视觉系统的相似性，以适应无监督的字典学习算法来创建专门用于降噪的字典。使用K奇异值分解（K-SVD）算法，我们通过学习噪声图像的小波分解获得自适应字典。在基准测试图像上的实验结果表明，我们提出的方法实现了非常有竞争力的降噪性能，并且优于最新的降噪方法，尤其是在峰值信噪比（PSNR），结构相似性（SSIM）指数和视觉效果方面不同噪声水平的效果。