基于Huber范数和概率运动场的鲁棒图像超分辨重建算法

Robust Image Super-resolution Reconstruction Algorithm Based on Huber Norm and Probabilistic Motion Field

传统超分辨(SR)算法对配准误差、模型误差以及噪声过于敏感,这限制了其在实际中的应用。为了提高算法的鲁棒性,该文从配准和重建两方面对传统算法进行了改进。在配准阶段,通过引入概率运动场避免了算法对配准精度的依赖,同时利用Heaviside函数实现权重映射,进一步提高了算法的自适应性;在重建阶段,采用基于Huber范数的正则化估计,在提高重建鲁棒性的同时也保证了算法数值解的稳定性。实验表明该算法具有很好的鲁棒性,其重构性能优于现有的一些超分辨重建方法。

The traditional Super-Resolution （SR） algorithms are very sensitive to image registration errors, model errors or noise, which limits their real utility. To enhance the robustness of SR algorithm, this paper improves the traditional SR algorithm from two aspects of image registration and reconstruction. On registration phase, the probabilistic motion field is introduced to prevent the SR algorithm from depending on accuracy of registration. In addition, the Heaviside function is adopted to implement the motion weight mapping, which enhances self-adaption of the algorithm further. On reconstruction phase, a regularized estimation based on Huber norm is used to reconstruct the SR image, which makes the proposed algorithm more stable to minimize the cost function while still robust against large errors. The experimental results show that the proposed algorithm has a good performance on sequence SR reconstruction compared with some existing SR methods.