A sharp edge-preserving joint color demosaicking and zooming algorithm using integrated gradients and an iterative back-projection technique

Abstract

Following the advances in single-sensor imaging techniques, interest in producing a zoomed full-color image from a Bayer mosaic data has been increased. Almost all of the recent approaches identified, with respect to the demosaicking step in the imaging pipeline, have chiefly focused on misguidance problems. However, in regions consisting of sharp edges or fine textures, these approaches are prone to large blurring effects. This paper proposes a new joint solution to overcome the above problems associated with demosaicking and zooming operations. On the basis of an enhanced soft-decision framework, we estimate the edge features by computing the integrated gradients. This allows the extraction of gradient information from both color intensity and color difference domains, simultaneously. Then, the edge guidance is incorporated in the interpolation of various stages to preserve edge consistency and improve computational efficiency. In addition, an edge-adaptive, iterative, back-projection technique is developed to compensate for image blurring as well as to further suppress color artifacts. Experimental results indicate that the new algorithm produces outstanding objective performances and sharp, visually pleasing color outputs, when compared to numerous other single-sensor image zooming solutions.