Xie Mengying, Liu Xiaolan, Yang Xiaowei
IEEE Trans Neural Netw Learn Syst. 2022 May 11;PP. doi: 10.1109/TNNLS.2022.3172184.
Multichannel image completion with mixture noise is a challenging problem in the fields of machine learning, computer vision, image processing, and data mining. Traditional image completion models are not appropriate to deal with this problem directly since their reconstruction priors may mismatch corruption priors. To address this issue, we propose a novel nonlocal self-similarity-based weighted tensor low-rank decomposition (NSWTLD) model that can achieve global optimization and local enhancement. In the proposed model, based on the corruption priors and the reconstruction priors, a pixel weighting strategy is given to characterize the joint effects of missing data, the Gaussian noise, and the impulse noise. To discover and utilize the accurate nonlocal self-similarity information to enhance the restoration quality of the details, the traditional nonlocal learning framework is optimized by employing improved index determination of patch group and handling strip noise caused by patch overlapping. In addition, an efficient and convergent algorithm is presented to solve the NSWTLD model. Comprehensive experiments are conducted on four types of multichannel images under various corruption scenarios. The results demonstrate the efficiency and effectiveness of the proposed model.
含混合噪声的多通道图像修复是机器学习、计算机视觉、图像处理和数据挖掘领域中的一个具有挑战性的问题。传统的图像修复模型由于其重建先验可能与损坏先验不匹配，因此不适合直接处理这个问题。为了解决这个问题，我们提出了一种基于非局部自相似性的加权张量低秩分解（NSWTLD）新模型，该模型可以实现全局优化和局部增强。在所提出的模型中，基于损坏先验和重建先验，给出了一种像素加权策略，以表征缺失数据、高斯噪声和脉冲噪声的联合效应。为了发现并利用准确的非局部自相似性信息来提高细节的恢复质量，通过采用改进的补丁组索引确定方法和处理补丁重叠引起的条带噪声，对传统的非局部学习框架进行了优化。此外，还提出了一种高效且收敛的算法来求解NSWTLD模型。在各种损坏场景下，对四种类型的多通道图像进行了综合实验。结果证明了所提出模型的有效性和高效性。
