Artifact-free image reconstruction for satellite imagery
Masato Ishii, Takashi Shibata, Atsushi Sato · 2016
This paper presents a novel image-reconstruction (IR) method for satellite imagery. To reduce unnatural artifacts, which often appear in existing IR methods, we propose a novel scheme to accurately estimate PSF and a novel regularization for IR. In the PSF estimation, hyper-parameters are estimated to reduce artifacts while improving sharpness based on our new criterion, which employs a residual of sigmoidal-function fitting to a strong edge on the reconstructed image as measurement of the amount of the artifacts. After the PSF estimation, we conduct IR based on the estimated PSF. In IR process, we employ a novel regularization that induces gradients of the reconstructed image to be close to those of its guide image. Since the guide image contains only the dominant structure of the input image without artifacts, the regularization leads to fewer artifacts. In addition, we employ a learning-based method for setting the spatially adaptive strength of the regularization effectively. Experimental results on real satellite imageries show that our method works better than other state-of-the-art IR methods.