Abstract In dictionary learning for analysis of images, spatial correlation from extracted patches can be leveraged to improve characterization power. We propose a Bayesian framework for dictionary learning, with spatial location dependencies captured by imposing a multiplicative Gaussian process (GP) priors on the latent units representing binary activations. Data augmentation and Kronecker methods allow for efficient Markov chain Monte Carlo sampling. We further extend the model with Sigmoid Belief Networks (SBNs), linking the GPs to the top-layer latent binary units of the SBN, capturing inter-dictionary dependencies while also yielding computational savings. Applications to image denoising, inpainting and depth-information restoration demonstrate that the proposed model outperforms other leading Bayesian dictionary learning approaches.