%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{piras.marelli.ea:2023,
         author = {Piras, Florian and Marelli, Fran{\c c}ois and De Moura Presa, Edouard and Wellig, Peter and Liebling, Michael},
       keywords = {artifact removal, deconvolution, thermal imaging},
       projects = {Idiap},
          month = oct,
          title = {Multi-image deconvolution of thermal images with a boundary condition weighting scheme},
      booktitle = {Target and Background Signatures IX},
         volume = {12736},
           year = {2023},
          pages = {149-158},
      publisher = {SPIE},
       location = {Amsterdam},
   organization = {International Society for Optics and Photonics},
            url = {https://doi.org/10.1117/12.2680294},
            doi = {10.1117/12.2680294},
       abstract = {Handheld thermal imaging devices can capture images in quick succession, each image with a slightly different orientiation, resulting in image series that can be combined to produce an improved image through multi-image deconvolution. 
Implementing deconvolution algorithms that take advantage of the entire information contained in the image series to produce an image with a field of view that is as large as possible given the coverage of all collected images is a challenging problem given that the image series covers a possibly non-square area.
In this paper, we present a multi-image deconvolution method that addresses this boundary condition problem.
First, we determine the relative geometric transformations between the images to determine a rectangular canvas that can accommodate the full field of view covered by the image series. Next, we formulate the deconvolution problem as a regularized minimization problem with two terms, (i) the residue between the forward transformation applied to the reconstructed candidate and the measured images and (ii) a regularization term to take into account image priors.
In order to accommodate for a non-square coverage of the combined images, which results in boundary artifacts when the forward model is used during iterative minimization, we recast the problem into one where the original scene is masked, thereby mitigating the effects of unknown image values beyond image boundaries.
We characterize our method on both synthetic and experimental images. 
We observe both visual and quantitative improvements of the images at the boundaries where distortions are attenuated.}
}