Least-squares reverse-time migration (LSRTM) distinctly improves seismic imaging quality, but at an expensive computation overhead involving tens of iterations. We herein take a computationally cheaper single-step LSRTM solution, which intrinsically performs deblurring through a data-domain Wiener deconvolution. However, the Wiener filter mainly solves the signal estimation problems for stationary signals. Subsalt imaging often suffers from strong salt-related reflections and artifacts. The former and the latter give rise to strong amplitude variance and changed source wavelets in the demigrated data, increasing its nonstationarity and hindering the data-deblurring operation in the single-step LSRTM. To alleviate the nonstationarity during the data-domain deblurring, we consider a Gabor deconvolution method. Testing on the Sigsbee data sets shows that the Gabor deconvolution method is effective, producing subsalt images of more balanced events and fewer artifacts than the raw RTM image. The Gabor deconvolution-related result also outperforms the standard single-step LSRTM result with more robust behavior and better subsalt imaging quality.