多帧图像超分辨理论是通过融合多帧低分辨率图像的信息来重构高空间分辨率图像。准确估计低分辨率图像的模糊核是进行有效信息融合的先决条件。传统超分辨方法通常假设模糊核已知且采用固定的高斯滤波模糊核, 并且模型参数需要费时的手动调整。本文在变分贝叶斯框架下获得相应的超分辨算法, 该算法对高分辨率图像、模糊核和模型参数同时进行最优估计。对比实验表明, 模糊核自适应估计的盲超分辨方法总体性能优于现有的变分贝叶斯框架下的图像超分辨方法, 特别是在高信噪比场景, 推荐方法优势更加明显。

Multi-frame image super-resolution method fuses the information of multi-frame low-resolution images to reconstruct high-resolution images. For multi-frame image super-resolution, the accurate estimation of blur kernel of low-resolution image is prerequisite for efficiency information fusion. Traditional super-resolution method usually assumes a known blur kernel and uses the Gaussian filter blur kernel for the enhancement. It also needs to tune the parameters by time-consuming hand-tuning. The proposed method acquires the super-resolution method based on the variational Bayesian method. The high-resolution image, the blur kernel and the model parameters are estimated simultaneously and automatically in the optimal stochastic sense. Experiments and simulations demonstrate that the proposed blind super-resolution method based on blur kernel self-adaptive estimation outperforms the state-of-art super-resolution method in variational Bayesian framework, especially, for the high signal to noise ratio scenarios.