Block‐Based MAP Superresolution Using Feature‐Driven Prior Model
Feng Xu, Tanghuai Fan, Chenrong Huang, Xin Wang, Lizhong Xu · Mathematical Problems in Engineering · 2014
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.