Inversion restoration for space diffractive membrane imaging system

Abstract We proposed a novel inversion restoration method for the newly developing space diffractive membrane imaging system, in which specific imaging characteristics were first integrated into the degradation model. On this basis, a novel image inversion restoration model was established based on sparse regularization frames and matrixing confidence parameters. Then, an efficient solution method was proposed using an improved iterative shrinkage-thresholding algorithm. Experimentally, when the diffraction efficiency was higher than 60%, the proposed inversion restoration method exhibited a satisfactory processing performance, and could achieve multi-objective image quality improvement, including texture detail restoration, background radiation removal, and noise suppression.

[1]  B. Zhang,et al.  Optimization method of multilayer diffractive optical elements with consideration of ambient temperature. , 2018, Applied optics.

[2]  Gordon Wetzstein,et al.  ProxImaL , 2016, ACM Trans. Graph..

[3]  Dawei Wang,et al.  Image degradation characteristics and restoration based on regularization for diffractive imaging , 2017 .

[4]  Wei Zhang,et al.  Influence of ambient temperature on the modulation transfer function of an infrared membrane diffraction optical system. , 2018, Applied optics.

[5]  Wolfgang Heidrich,et al.  Computational imaging using lightweight diffractive-refractive optics. , 2015, Optics express.

[6]  G. Andersen.  Large optical photon sieve. , 2005, Optics letters.

[7]  Alberto Diaspro,et al.  Fuzzy logic and maximum a posteriori-based image restoration for confocal microscopy. , 2006, Optics letters.

[8]  Effects of mask-alignment error on point spread function for multi-level Fresnel diffractive lenses , 2018 .

[9]  Brian Patrick,et al.  Large-aperture fast multilevel Fresnel zone lenses in glass and ultrathin polymer films for visible and near-infrared imaging applications. , 2014, Applied optics.

[10]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[11]  Yue Zhang,et al.  Transmissive diffractive membrane optic for large aperture lightweight optical telescope , 2015, International Conference on Optical Instruments and Technology.

[12]  Brian Bouzas,et al.  Objective image quality measure derived from digital image power spectra , 1992 .

[13]  Paul D. Atcheson,et al.  MOIRE: Ground Test Bed Results for a Large Membrane Telescope , 2014 .

[14]  Rob Fergus,et al.  Blind deconvolution using a normalized sparsity measure , 2011, CVPR 2011.

[15]  R. Hyde,et al.  Eyeglass. 1. Very large aperture diffractive telescopes. , 1999, Applied optics.

[16]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[17]  Hongming Zhang,et al.  Construction model for total variation regularization parameter. , 2014, Optics express.