The paper presents an image denoising scheme by combining a method that is based on directional quasi-analytic wavelet packets (qWPs) with the state-of-the-art Weighted Nuclear Norm Minimization (WNNM) denoising algorithm. The qWP-based denoising method (qWPdn) consists of multiscale qWP transform of the degraded image, application of adaptive localized soft thresholding to the transform coefficients using the Bivariate Shrinkage methodology, and restoration of the image from the thresholded coefficients from several decomposition levels. The combined method consists of several iterations of qWPdn and WNNM algorithms in a way that at each iteration the output from one algorithm boosts the input to the other. The proposed methodology couples the qWPdn capabilities to capture edges and fine texture patterns even in the severely corrupted images with utilizing the non-local self-similarity in real images that is inherent in the WNNM algorithm. Multiple experiments, which compared the proposed methodology with six advanced denoising algorithms, including WNNM, confirmed that the combined cross-boosting algorithm outperforms most of them in terms of both quantitative measure and visual perception quality.

翻译：本文提出了一种图像去噪方案，将基于方向性准解析小波包（qWPs）的方法与当前最先进的加权核范数最小化（WNNM）去噪算法相结合。基于qWP的去噪方法（qWPdn）包括：对降质图像进行多尺度qWP变换，利用双变量收缩方法对变换系数进行自适应局部软阈值处理，以及从多个分解层的阈值化系数恢复图像。该组合方法通过多次迭代qWPdn和WNNM算法实现，每次迭代中，一个算法的输出增强另一个算法的输入。所提出的方法将qWPdn在严重退化图像中捕获边缘和精细纹理模式的能力，与WNNM算法中利用真实图像非局部自相似性的固有优势相结合。多项实验将所提方法与包括WNNM在内的六种先进去噪算法进行了比较，证实了这种组合交叉增强算法在定量指标和视觉感知质量上均优于大多数对比算法。