Image reconstruction based on indirect, noisy, or incomplete data remains an important yet challenging task. While methods such as compressive sensing have demonstrated high-resolution imagerecovery in various settings, there remain issues of robustness due to parameter tuning. Moreover, since the recovery is limited to a point estimate, it is impossible to quantify the uncertainty,which is often desirable. Due to these inherent limitations, a sparse Bayesian learning approach issometimes adopted to recover a posterior distribution of the unknown. Sparse Bayesian learningassumes that some linear transformation of the unknown is sparse. However, most of the methods developed are tailored to specific problems, with particular forward models and priors. Here,we present a generalized approach to sparse Bayesian learning. It has the advantage that it can beused for various types of data acquisitions and prior information. Some preliminary results on imagereconstruction/recovery indicate its potential use for denoising, deblurring, and magnetic resonanceimaging.