Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In large-scale spatial surveys, the point spread function (PSF) varies across the instrument field of view (FOV). Local measurements of the PSFs are given by the isolated star images. Yet, these estimates may not be directly usable for post-processing because of the observational noise and potentially the aliasing. Given a set of aliased and noisy star images from a telescope,  aim to estimate ll-resolved and noise-free PSFs at the observed star positions. In particular, exploiting the spatial correlation of the PSFs across the FOV.  introduce RCA (resolved components analysis) which is a noise-robust dimension reduction and super-resolution method based on matrix-factorization.  propose an original way of using the PSFs spatial correlation in the restoration process through sparsity. The introduced formalism can be applied to correlated data sets with respect to any euclidean parametric space.  tested our method on simulated monochromatic PSFs of Euclid telescope (launch planned for 2020). The proposed method outperforms existing PSFs restoration and dimension reduction methods.  show that a coupled sparsity constraint on individual PSFs and their spatial distribution yields a significant improvement on both the restored PSFs shapes and the PSFs subspace identification, in presence of aliasing. RCA can be naturally extended to account for the wavelength dependency of the PSFs.
['F. M. Ngolè Mboula', 'Jean-Luc Starck', 'Kagao Okumura', 'J. Amiaux', 'P. Hudelot']
I am writing a research paper on a idea Constraint matrix factorization for space variant PSFs field restoration
[{"from": "human", "value": "I am writing a research paper on a idea Constraint matrix factorization for space variant PSFs field restoration"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In large-scale spatial surveys, the point spread function (PSF) varies across the instrument field of view (FOV). Local measurements of the PSFs are given by the isolated star images. Yet, these estimates may not be directly usable for post-processing because of the observational noise and potentially the aliasing. Given a set of aliased and noisy star images from a telescope,  aim to estimate ll-resolved and noise-free PSFs at the observed star positions. In particular, exploiting the spatial correlation of the PSFs across the FOV.  introduce RCA (resolved components analysis) which is a noise-robust dimension reduction and super-resolution method based on matrix-factorization.  propose an original way of using the PSFs spatial correlation in the restoration process through sparsity. The introduced formalism can be applied to correlated data sets with respect to any euclidean parametric space.  tested our method on simulated monochromatic PSFs of Euclid telescope (launch planned for 2020). The proposed method outperforms existing PSFs restoration and dimension reduction methods.  show that a coupled sparsity constraint on individual PSFs and their spatial distribution yields a significant improvement on both the restored PSFs shapes and the PSFs subspace identification, in presence of aliasing. RCA can be naturally extended to account for the wavelength dependency of the PSFs."}]