Frequency-domain Regularized Deconvolution for Images with Stripe Noise

This paper presents a new approach to the deconvolution for images contaminated by stripe noise. Inspired by the 2D power spectrum distribution property of stripe noise in the frequency domain, we construct a novel regularized inverse filter which allows the algorithm to suppress the amplification of stripe noise in the Fourier inverse step and further get rid of most of them, and a mirror-wavelet denoising is followed to remove the left colored noise. In simulations with striped images, this algorithm outperforms the traditional mirror-wavelet based deconvolution in terms of both visual effect and SNR comparison, only at the expense of slightly heavier computation load. The same idea about regularized inverse filter can also be used to improve other deconvolution algorithms, such as wavelet packets and Wiener filters, when they are employed to images stained by stripe noise.

[1]  L. Landweber.  An iteration formula for Fredholm integral equations of the first kind , 1951 .

[2]  C. Helstrom.  Image Restoration by the Method of Least Squares , 1967 .

[3]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

[4]  L. Lucy.  An iterative technique for the rectification of observed distributions , 1974 .

[5]  J. Högbom,et al.  APERTURE SYNTHESIS WITH A NON-REGULAR DISTRIBUTION OF INTERFEROMETER BASELINES. Commentary , 1974 .

[6]  Multichannel Maximum Entropy Spectral Analysis Using Least Squares Modelling , 1986 .

[7]  Alexander N. Tikhonov,et al.  III-posed image processing problems , 1987 .

[8]  Richard G. Lane,et al.  Automatic multidimensional deconvolution , 1987 .

[9]  T J Holmes,et al.  Blind deconvolution of quantum-limited incoherent imagery: maximum-likelihood approach. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[10]  Timothy J. Schulz,et al.  Multiframe blind deconvolution of astronomical images , 1993 .

[11]  Fionn Murtagh,et al.  Image restoration with noise suppression using the wavelet transform , 1994 .

[12]  D. Donoho.  Nonlinear Solution of Linear Inverse Problems by Wavelet–Vaguelette Decomposition , 1995 .

[13]  Deepa Kundur,et al.  Blind Image Deconvolution , 2001 .

[14]  Jean-Luc Starck,et al.  Deconvolution of astronomical images using the multiscale maximum entropy method , 1996 .

[15]  Josiane Zerubia,et al.  Satellite image deconvolution using complex wavelet packets , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[16]  Fionn Murtagh,et al.  Deconvolution based on the curvelet transform , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[17]  Hideo Saito,et al.  Extraction of Dermo-Epidermal Surface from 3D Volumetric Images of Human Skin , 2003, Int. J. Image Graph..

[18]  Stéphane Mallat,et al.  Deconvolution by thresholding in mirror wavelet bases , 2003, IEEE Trans. Image Process..

[19]  B. Rouge,et al.  Image de-blurring and application to SPOT5 THR satellite imaging , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[20]  Huadong Guo,et al.  Destriping CMODIS data by power filtering , 2003, IEEE Trans. Geosci. Remote. Sens..

[21]  Brian Klinkenberg,et al.  A Spatial Filter for the Removal of Striping Artifacts in Digital Elevation Models , 2003 .

[22]  Zelin Shi,et al.  Adaptively image de-striping through frequency filtering , 2006, International Commission for Optics.

[23]  Ming Yan,et al.  Ultrasound image deconvolution in symmetrical mirror wavelet bases , 2006, SPIE Medical Imaging.