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INPAINTING FOR REMOTELY SENSED IMAGES WITH A MULTICHANNEL
NONLOCAL TOTAL VARIATION MODEL
ABSTRACT:
Filling dead pixels or removing uninteresting objects is often desired in the applications of
remotely sensed images. In this paper, an effective image inpainting technology is presented
to solve this task, based on multichannel nonlocal total variation. The proposed approach
takes advantage of a nonlocal method, which has a superior performance in dealing with
textured images and reconstructing large-scale areas. Furthermore, it makes use of the
multichannel data of remotely sensed images to achieve spectral coherence for the
reconstruction result. To optimize the proposed variation model, a Bregmanized-operator-splitting
algorithm is employed. The proposed inpainting algorithm was tested on simulated
and real images. The experimental results verify the efficacy of this algorithm.