Reconstruction of High Resolution image from overlapping Low Resolution images

Super-resolution image

In video surveillance, microscopy, image restoration and many other applications, High-Resolution (HR) images are required when only Low-Resolution (LR) images are available. This situation arises when details to be analyzed are beyond the resolution of the imager.
Multi-Frame Super-Resolution (MFSR) is a way to overcome this problem. It consists in reconstructing an HR image from one or several overlapping LR images of the same scene acquired with the same imager. This technique relies on the fact that several images acquired from the same scene contain different information when there are slight motions between the images.
To achieve MFSR, LR images have to be sub-pixel registered and merged. Then, a deconvolution process enables the reversal of the blur induced by the so-called Point Spread Function (PSF) of the imager. Until now, this last procedure relied heavily on the precise modeling of the imager’s PSF. However, identifying this PSF is nearly impossible. The proposed method does not need such a model. It uses the new mathematical tool we developed, called maxitive kernels, to achieve an imprecise modeling of the PSF based on very basic and generic information about the imager. One particularity of this method is its ability to produce an interval-valued reconstructed HR image. We have proposed a-posteriori regularization for selecting the most representative image in this convex set of HR images. This method possesses a robustness advantage over its competitors with respect to modeling, registration, overfitting and artifacts.
[1] F. Graba, F. Comby, O. Strauss: Non-Additive Imprecise Image Super-Resolution in a Semi-Blind Context. IEEE Trans. Image Processing 26(3): 1379-1392 (2017)