Monte Carlo rendering often faces a dilemma, namely, whether to choose an unbiased estimator or a biased one. Although different integrators have been developed to address various scenarios, no single method can effectively manage all situations. Thus, finding a good approach to combine different integrators has always been a topic that warrants exploration. This work proposes DSCombiner, a new shrinkage estimator that flexibly combines unbiased and biased estimators (typically generated by different integrators) in image space into a single estimating procedure, strategically utilizing the strengths of different integrators while minimizing their weaknesses. DSCombiner overcomes the limitation of single shrinkage combiners by introducing a two-step shrinkage towards a noise-free radiance prior. We derive optimal shrinkage factors for the two steps within a hierarchical Bayesian framework, and provide a deep learning-based method to improve the results. Comprehensive qualitative and quantitative validations across diverse scenes demonstrate visible improvements in image quality, as compared with previous image-space and path-space combiners.