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JBE, vol. 23, no. 1, pp.146-153, January, 2018
Regularization-based Superresolution Demosaicing using Aperture Mask Wheels
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This paper presents a superresolution demosaicing technique that can restore high-resolution color image from differently blurred low resolution images in Bayer domain. The proposed superresolution demosaicing algorithm uses an aperture mask wheel to get differently blurred low resolution images, so we just need to estimate point spread function at each frame. In addition, it does not require image registration because there is no translational motion between low resolution images. By using a rotatable aperture mask wheel, consecutive captured images provide sufficiently exclusive information for superresolution. Therefore, the proposed method can reduce the registration error between the low-resolution image as well as the calculation amount for superresolution restoration. The existing lens system of the camera can be extended to obtain a superresolution image by only adding an rotatable aperture mask wheels. Finally, in order to verify the performance of the proposed system, experimental results are performed. The proposed method showed the significant improvements in the sense of spatial and color resolution.
Keyword: superresolution, demosaicing, image restoration, aperture mask
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