Block-Based MAP Superresolution Using Feature-Driven Prior Model

Abstract EN

In the field of image superresolution reconstruction (SRR), the prior can be employed to solve the ill-posed problem.

However, the prior model is selected empirically and characterizes the entire image so that the local feature of image cannot be represented accurately.

This paper proposes a feature-driven prior model relying on feature of the image and introduces a block-based maximum a posteriori (MAP) framework under which the image is split into several blocks to perform SRR.

Therefore, the local feature of image can be characterized more accurately, which results in a better SRR.

In process of recombining superresolution blocks, we still design a border-expansion strategy to remove a byproduct, namely, cross artifacts.

Experimental results show that the proposed method is effective.