Neural network‐based image restoration using scaled residual with space‐variant regularization

Image restoration is aimed to recover the original scene from its degraded version. This paper presents a new method for image restoration. In this technique, an evaluation function which combines a scaled residual with space‐variant regularization is established and minimized using a Hopfield network to obtain a restored image from a noise corrupted and blurred image. Simulation results demonstrate that the proposed evaluation function leads to a more efficient restoration process which offers a fast convergence and improved restored image quality. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 247–253, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10034

[1]  Dansheng Song,et al.  Adaptive Neural Network for Nuclear Medicine Image Restoration , 1998, J. VLSI Signal Process..

[2]  D. M. Titterington,et al.  A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Tommy W. S. Chow,et al.  Improved blind image restoration scheme using recurrent filtering , 2000 .

[4]  A. Murat Tekalp,et al.  Maximum likelihood image and blur identification: a unifying , 1990 .

[5]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Stanley J. Reeves,et al.  Optimal space-varying regularization in iterative image restoration , 1994, IEEE Trans. Image Process..

[7]  R. Mersereau,et al.  Optimal estimation of the regularization parameter and stabilizing functional for regularized image restoration , 1990 .

[8]  Reginald L. Lagendijk,et al.  Regularized iterative image restoration with ringing reduction , 1988, IEEE Trans. Acoust. Speech Signal Process..

[9]  B. K. Jenkins,et al.  Image restoration using a neural network , 1988, IEEE Trans. Acoust. Speech Signal Process..

[10]  Richard J. Mammone,et al.  An Iterative Projection Technique for Blind Image Restoration , 1993, J. Vis. Commun. Image Represent..

[11]  A. Murat Tekalp,et al.  Adaptive image restoration with artifact suppression using the theory of convex projections , 1990, IEEE Trans. Acoust. Speech Signal Process..

[12]  Yitzhak Yitzhaky,et al.  Identification of Blur Parameters from Motion Blurred Images , 1997, CVGIP Graph. Model. Image Process..

[13]  Rokuya Ishii,et al.  Restoration of degraded images with neural networks , 1998 .

[14]  Ling Guan,et al.  Adaptive regularization in image restoration using a model-based neural network , 1997 .

[15]  Aggelos K. Katsaggelos,et al.  A regularized iterative image restoration algorithm , 1991, IEEE Trans. Signal Process..