Compressive hyperspectral imaging (CHI) with random encoding mask usually suffers from various noises and artifacts. Inspired by the dual-camera CHI techniques based on hyperspectral (HS) and multispectral (MS) image fusion, herein, we present a single-camera push-broom CHI method based on self-fusion refinement (SFR). In this work, the MS guidance image for data fusion is derived directly from the raw solved HS data cube itself rather than any additional data source, which turns cross-fusion into self-fusion; furthermore, a modified joint bilateral filtering (JBF) fusion algorithm is developed to adapt this self-fusion problem, and an adaptive range Gaussian radius is adopted to avoid the invalidation or over-smoothing effects so as to ensure spatial and spectral improvement. The visualized and quantitative assessment results both demonstrate that the proposed method achieves high-quality HS imaging in terms of noise and artifact removal and spatial-spectral fidelity. Furthermore, the proposed method has a great flexibility and extensibility, whose performances highly depend on the exact fusion algorithm adopted, and a more suitable fusion algorithm will lead to better reconstruction quality; herein, the SFR process by the modified JBF achieves better performances than SFR by guided filtering (GF) or Markov random field (MRF).