Texture and edge preserving multiframe super‐resolution
Emre Turgay, Gözde Bozdağı Akar · IET Image Processing · 2014
Super‐resolution (SR) image reconstruction refers to methods where a higher resolution image is reconstructed using a set of overlapping aliased low‐resolution observations of the same scene. Although edge preservation has been a widely explored topic in SR literature, texture‐specific regularisation has recently gained interest. In this study, texture‐specific regularisation is handled as a post‐processing step. A two stage method is proposed, comprising multiple SR reconstructions with different regularisation parameters followed by a restoration step for preserving edges and textures. In the first stage, two maximum‐a‐posteriori estimators with two different amounts of regularisation are employed. In the second stage, pixel‐to‐pixel difference between these two estimates is post‐processed to restore edges and textures. Frequency selective characteristics of discrete cosine transform and Gabor filters are utilised in the post‐processing step. Experiments on synthetically generated images and real experiments demonstrate that the proposed methods give better results compared with the state‐of‐the‐art SR methods especially on textures and edges.