摘要
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 a compressed sensing super-resolution reconstruction algorithm,called Curvelet-FIST,which is based on Curvelet transform and fast iterative threshold-shrinkage( FIST) 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 from sampling values through FIST algorithm. Simulation experiment showed that this super-resolution reconstruction algorithm,compared with traditional interpolation algorithm and the compressed sensing reconstruction algorithm based on Wavelet transform and FIST( Wavelet-FIST),has higher peak signal-to-noise ratio( PSNR).
引文
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