基于Curvelet变换的压缩传感超分辨率重建

COMPRESSED SENSING SUPER-RESOLUTION RECONSTRUCTION BASED ON CURVELET TRANSFORM

为了在无训练集的情况下,改善单帧退化图像的分辨率,实现了一种基于Curvelet变换和快速迭代收缩阈值法(FIST)的压缩传感超分辨率重建算法(Curvelet-FIST)。算法首先对低分辨率图像建立伪星形采样的采样方式,利用压缩传感理论,在Curvelet变换域,通过快速迭代收缩阈值法由采样值恢复出高分辨率图像。仿真实验表明,此超分辨率重建算法比传统的插值算法以及基于Wavelet变换和FIST的压缩传感重建算法(Wavelet-FIST)有更高的峰值信噪比。

In order to improve the resolution of single-frame degraded images under the condition of no any training set, we implemented acompressed sensing super-resolution reconstruction algorithm, called Curvelet-FIST, which is based on Curvelet transform and fast iterativethreshold-shrinkage （F IST ） algorithm. First, the algorithm sets up a sampling mode of pseudo-star-shape sampling on low-resolution images.Then by making use of the theory of compressed sensing, and in Curvelet transform domain, it restores the high-resolution image fromsampling values through FIST algorithm. Simulation experiment showed that this super-resolution reconstruction algorithm, compared withtraditional interpolation algorithm and the compressed sensing reconstruction algorithm based on Wavelet transform and FIST （W avelet-FIST）,has higher peak signal-to-noise ratio （P SN R ）.