Image restoration with a microscanning imaging system

Traditional methods of image restoration use only one observed image for processing. In this paper, we propose methods for image restoration using several distorted images obtained with a microscanning imaging system. We assume that the observed images contain an original image degraded either by additive or by multiplicative interferences. Additionally, the images are corrupted with the additive noise of a receiver sensor. Using a set of observed images, image restoration is carried out by solving a system of equations that is derived from optimization of an objective function. Since the proposed restoration methods possess a high computational complexity, a fast algorithm is proposed. Computer simulation results obtained with the proposed methods are analyzed in terms of restoration accuracy, tolerance to the additive input noise, and robustness to sensors’position errors.

[1]  Shree K. Nayar,et al.  Contrast Restoration of Weather Degraded Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Dana H. Brooks,et al.  Electrical imaging of the heart , 1997, IEEE Signal Process. Mag..

[3]  Vitaly Kober,et al.  Image restoration based on camera microscanning , 2008, Optical Engineering + Applications.

[4]  Aggelos K. Katsaggelos,et al.  Digital image restoration , 2012, IEEE Signal Process. Mag..

[5]  Carlos Ferreira,et al.  Optical implementation of the weighted sliced orthogonal nonlinear generalized correlation for nonuniform illumination conditions. , 2002, Applied optics.

[6]  J. L. López-Martínez,et al.  Image restoration based on camera microscanning , 2010, Pattern Recognition and Image Analysis.

[7]  Vitaly Kober,et al.  Fast image restoration algorithm based on camera microscanning , 2009, Optical Engineering + Applications.

[8]  J. L. López-Martínez,et al.  Image restoration of nonuniformly illuminated images with camera microscanning , 2010, Optical Engineering + Applications.

[9]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[10]  Stephen E Reichenbach,et al.  Small-kernel superresolution methods for microscanning imaging systems. , 2006, Applied optics.

[11]  Vitaly Kober,et al.  Target recognition under nonuniform illumination conditions. , 2009, Applied optics.

[12]  N. Hautiere,et al.  Contrast restoration of foggy images through use of an onboard camera , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[13]  Gene H. Golub,et al.  Matrix computations , 1983 .

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

[15]  Bradley M. Ratliff,et al.  An algebraic algorithm for nonuniformity correction in focal-plane arrays. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  Trac D. Tran,et al.  A new resampling approach for optimal reconstruction , 2009, Optical Engineering + Applications.

[17]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.

[18]  Vitaly Kober,et al.  Nonlinear filters with spatially-connected neighborhoods , 2001 .

[19]  B K Horn,et al.  Calculating the reflectance map. , 1979, Applied optics.

[20]  A. Ferrero,et al.  Principal components analysis of the photoresponse nonuniformity of a matrix detector. , 2007, Applied optics.

[21]  Tae-Sun Choi,et al.  Improved motion stereo matching based on a modified dynamic programming , 2001 .

[22]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .