Abstract: The focus of seismic exploration has gradually shifted from structural reservoirs to concealed reservoirs, which raises high requirements for high-resolution processing technology.Conventional high-resolution processing techniques, such as deconvolution and inverse Q filtering, are usually based on one-dimensional assumptions and are effective for horizontal structures or small dip-angle structures.However, these techniques are invalid theoretically for complex structures such as high-steep structures or faults.As we know, the migration image is the convolution of point spread function (PSF) and true reflection coefficient.The high-resolution processing technology in high-dimensional space is actually an inverse problem of this forward modeling problem, which is to solve the reflectivity based on migration result.In this paper, the high-resolution processing technology of imagery based on PSF is proposed.Firstly, a fast algorithm for solving the PSF in high-frequency approximation is deduced according to the computing theory of PSF.Then, a purely data-driven PSF extraction technique is developed depending on the theory of deconvolution in high-dimensional space together with the imagery.Finally, the high-dimensional space deconvolution algorithm is used to obtain high-resolution imaging result, so that the frequency band is widened.The tests of model and field data show that the algorithm proposed in this paper can effectively expand high frequencies without losing low frequencies and has the advantage of improving the resolution of geological bodies with different distribution azimuths compared with the conventional high-resolution algorithms.