基于快速l1-范数稀疏表示和TGV的超分辨算法研究

An anisotropic edge total generalized variation energy super-resolution based on fast l1--norm dictionary edge representations

针对光学相机成像分辨率低、噪声干扰严重等问题,本文提出一种能有效去噪的高精度超分辨方法-基于快速l1-范数稀疏表示和二阶广义全变分(TGV)的超分辨方法。首先利用各向异性扩散张量(ADT)作为边缘高频信息,通过快速l1-范数稀疏表示方法建立LR图像和相对应的高频信息ADT的字典集;其次将字典学习到的ADT边缘信息和TGV模型组合成新的规则项;最后利用新的规则项建立超分辨代价函数,并利用图像增强后处理方法对整幅图像进行优化。结果表明:算法对仿真数据和SO12233靶标数据具有较高的可行性和鲁棒性,能有效去除噪声等异常点,获得高质量清晰图像,同时与其他经典算法相比,所提算法超分辨的峰值信噪比和结构相似度均有所增大。

For camera-based imaging, low resolution and noise outliers are the major challenges. Here, we propose a novel super-resolution method-total generalized variation(TGV) super-resolution based on fast l1-norm dictionary edge representations. First, anisotropic diffusion tensor(ADT) is utilized as high frequency edge information. The fast l1-norm dictionary representation method is used to create dictionaries of LR image and the corresponding high frequency edge information. This method can quickly build dictionaries on the same database, and avoid the influence of outliers. Then we combine the edge information ADT and TGV model as the new regularization function. Finally, the super-resolution cost function is established. The results show that the algorithm has high feasibility and robustness to simulation data and SO12233 target data. It can effectively remove noise outliers and obtain high-quality clear images. Compared with other classical algorithms, the proposed algorithm can obtain higher PSNR and SSIM values.