Combining Fields of Experts (FoE) and K-SVD methods in pursuing natural image priors

Natural image prior is one of the most efficient ways to represent images for computer vision tasks. In the literature, filter response statistics prior and synthesis-based sparse representation are two dominant prior models, which have been investigated separately and our knowledge of the relation between these two methods remains limited. In this paper, we examine the inherent relationship between the Fields of Experts (FoE) and K-SVD methods in the pursuit of natural image priors. We theoretically analyze and show that these two prior models have a mutually complementary relationship in the pursuit of the structure of natural images space. Based on these findings, a novel joint statistical prior is proposed, in which adaptive filters are obtained by exploring clues from both priors and utilized to characterize the subtle structure of natural images subspace. Qualitative and quantitative experiments demonstrate that the proposed method achieves a more comprehensive and reliable estimation of natural image prior and is competitive to both alternative and state-of-the-art methods.