针对压缩遥感过程中非严格稀疏和傅里叶域欠采样噪声导致的伪影和混叠现象，提出了基于梯度转向核的压缩重构策略（GradSK）.在压缩感知编码过程中提出了半随机傅里叶测量的方式，既保留图像的概要分量，同时保证了K-空间随机欠采样的非连贯性.在压缩感知解码过程中提出了由基于多阶梯度的转向核与有限差分总方差（TV）结合的方法，来解决解码过程中的无约束凸框架问题.实验表明，该方法在解决无噪采样和有噪采样的过程中均有较好性能.

A gradient-based steering kernel (GradSK) reconstruction strategy for compressed remote sensing is proposed. It aims to solve the artifacts and blurriness caused by the none-strictly sparsity and the noisy Fourier undersamples. Semi-random Fourier measurements are presented for encoding, which can preserve approximating components of images and retain the incoherence by random undersamples in the periphery of K-space. The steering kernel derived from multistep gradients is exploited to encapsulate with finite-difference Total Variance (TV) in the unconstrained convex framework for decoding. Numerical results demonstrate the superior performance of this algorithm in the case of noiseless and noisy measurements for compressed remote sensing.