Efficient RGBW remosaicing using local interpolation and global refinement

We propose an efficient RGBW remosaicing algorithm that converts RGBW images into Bayer images using learned kernel-based local interpolation and global residual learning. First, the proposed algorithm extracts local and global features from an input RGBW image. Then, we develop a learned kernel-based interpolation module to generate an intermediate Bayer image using the local features. Next, the proposed algorithm generates a residual image containing complementary information. Finally, we obtain the reconstructed Bayer image by refining the intermediate Bayer image with the residual image. Experimental results demonstrate that the proposed algorithm significantly outperforms state-of-the-art algorithms.