IEEE Trans Image Process. 2018 Oct;27(10):4971-4986. doi: 10.1109/TIP.2018.2848113.
Multi-frame image super-resolution focuses on reconstructing a high-resolution image from a set of low-resolution images with high similarity. Combining image prior knowledge with fidelity model, the Bayesian-based methods have been considered as an effective technique in super-resolution. The minimization function derived from maximum a posteriori probability (MAP) is composed of a fidelity term and a regularization term. In this paper, based on the MAP estimation, we propose a novel initialization method for super-resolution imaging. For the fidelity term in our proposed method, the half-quadratic estimation is used to choose error norm adaptively instead of using fixed and norms. Besides, a spatial weight matrix is used as a confidence map to scale the estimation result. For the regularization term, we propose a novel regularization method based on adaptive bilateral total variation (ABTV). Both the fidelity term and the ABTV regularization guarantee the robustness of our framework. The fidelity term is mainly responsible for dealing with misregistration, blur, and other kinds of large errors, while the ABTV regularization aims at edge preservation and noise removal. The proposed scheme is tested on both synthetic data and real data. The experimental results illustrate the superiority of our proposed method in terms of edge preservation and noise removal over the state-of-the-art algorithms.
多帧图像超分辨率旨在从一组具有高相似度的低分辨率图像中重建出高分辨率图像。将图像先验知识与保真度模型相结合，基于贝叶斯的方法已被认为是超分辨率中的一种有效技术。由最大后验概率 (MAP) 导出的最小化函数由保真度项和正则化项组成。在本文中，我们基于 MAP 估计提出了一种新的超分辨率成像初始化方法。对于我们提出的方法中的保真度项，使用半二次估计自适应地选择误差范数，而不是使用固定的范数。此外，使用空间权重矩阵作为置信图来缩放估计结果。对于正则化项，我们提出了一种基于自适应双边全变分 (ABTV) 的新正则化方法。保真度项和 ABTV 正则化都保证了我们框架的稳健性。该保真度项主要负责处理配准错误、模糊和其他类型的大误差，而 ABTV 正则化则旨在保留边缘和去除噪声。该方案在合成数据和真实数据上进行了测试。实验结果表明，与最先进的算法相比，我们提出的方法在边缘保持和噪声去除方面具有优越性。
