Depth map super‐resolution via shape‐adaptive non‐local regression and direction‐based local smoothness

In this letter, a novel single depth map super‐resolution algorithm is proposed, which combines the non‐local prior and local smoothness prior. Unlike the color‐guided methods, the proposed method does not need a corresponding color image to aid the depth map super‐resolution. To explore the non‐local self‐similarity in the depth map, a shape‐adaptive adjusted non‐local regression is constructed using the shape‐adaptive similar patch groups. This prior can make full use of the non‐local information of the depth map and alleviate the effect of irrelevant pixels. To construct different local structures, the direction‐based local smoothness prior to model the different directional information is proposed, which can preserve the different structures in the depth map. Compared with the state‐of‐the‐art methods, experimental results indicate that the proposed approach can achieve better reconstruction performance.