Computer Science ›› 2019, Vol. 46 ›› Issue (1): 86-93.doi: 10.11896/j.issn.1002-137X.2019.01.013

• CCDM2018 • Previous Articles     Next Articles

Image Restoration Method Based on Improved Inverse Filtering for Diffractive Optic Imaging Spectrometer

ZHANG Ming-qi, CAO Guo, CHEN Qiang, SUN Quan-sen   

  1. (School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
  • Received:2018-06-08 Online:2019-01-15 Published:2019-02-25

Abstract: In order to solve the image-blurring problem caused by the interference from out-of-focus optical images in in-focus image within a diffractive optic imaging spectrometer (DOIS),an improved inverse filtering restoration method was proposed to solve the ill-posed problem in inverse filtering and restore the diffraction spectrum image.This method changes the solution of the primal problem by introducing a regularization matrix to regularize the inverse filtering function,thus suppressing the influences of noises on restored images.It achieves the purpose of reducing morbidity of the matrix and obtaining a better restoration result through the following three procedures:convert the image restoration process into a process of matrix inversion,add a regular filter to the SVD (singular value decomposition) method,and adjust the form of the regularization matrix and the values of parameters.Experiments show that the improved inverse filtering method is effective for restoring the spectral images formed with a diffractive optic imaging spectrometer.It can not only increase the Laplacian Gradient and QI(Quality Index) value,but also reduce RMSE(Root-Mean-Square Error) value to a certain extent.In the meantime,this method can suppress the noise interferences of the blurred images,enhance the image clarity,restore a single spectrum image with a higher similarity to the reference image,and obtain better spectral curves to analyze the geomorphological features.

Key words: Diffractive optic imaging spectrometer, Image restoration, Inverse filtering, Singular value decomposition

CLC Number: 

  • TP391
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