Multiframe super-resolution algorithms reconstruct high-resolution images by exploiting complementary information in multiple low-resolution frames. However, despite their success under ideal conditions, most existing methods rely on simplistic approximations to the physics of image acquisition and show limited robustness in real-world applications. This paper proposes spatially adaptive Bayesian modeling and an iterative algorithm for robust super-resolution imaging. In particular, we introduce a weighted Gaussian observation model to consider space variant noise and weighted bilateral total variation to exploit sparsity of natural images. Based on this model, we develop a majorization-minimization algorithm implemented as iteratively re-weighted minimization. The proposed method simultaneously estimates model parameters and the super-resolved image in an iterative coarse-to-fine scheme. Compared to prior work, our approach combines the benefits of achieving robust and edge preserving image reconstruction with small amount of parameter tuning, while being flexible in terms of motion models, computationally efficient and easy to implement. Our experimental evaluation confirms that our approach outperforms state-of-the-art algorithms under various practical conditions, e.g., inaccurate geometric and photometric registration or invalid measurements.