Description

ABSTRACT

Image deblurring is the process of obtaining the original image by using the knowledge of the degrading factors. Degradation comes in many forms such as blur, noise, and camera Mis-focus. A major drawback of existing restoration methods for images is that they suffer from poor convergence properties, the algorithms converge to local minima, that they are impractical for real imaging applications. Added to its disadvantage, some methods make restrictive assumptions on the PSF or the true image that limits the algorithm’s portability to different applications. In conventional approach, deblurring filters are applied on the degraded images without the knowledge of blur and its effectiveness. In this paper, concepts of artificial intelligence are applied for restoration problem in which images are degraded by a blur function and corrupted by random noise. The proposed methodology adopted back propagation network with gradient decent rule which consists of three layers. This methodology uses highly nonlinear back propagation neuron for image restoration to get a high quality restored image.

EXISTING SYSTEM

• Wiener filter
• Anisotropic Filter

DISADVANTAGES

• Only Provide a Point Estimate
• Can only handle processes with additive, Uni-model noise.

PROPOSED SYSTEM

• Median Filter
• Richardson-Lucy Algorithm
• Back Propagation Neural Network

ADVANTAGES

• To minimize the Mean Squared Estimation error more.
• Can be computed in real time

BLOCK DIAGRAM

SOFTWARE REQUIREMENTS

• Python
• Opencv
• Numpy

APPLICATION

• Satellite Application
• Surveillance Application

RESULT

This proposed system deals with the enhancement of quality of an image, so that the information in the image will high.the major application area of this proposed system is the processing on satellite images.

REFERENCE

[1] Ganglia project. http://ganglia.sourceforge.net, Accessed 10th March 2006.

[2] Gridice project. http://infnforge.cnaf.infn.it/gridice, Accesed 11th March 2006.

[3] Monalisa project. http://monalisa.cacr.caltech.edu, Accessed 15th March 2006.

[4] Rgma project. http://www.r-gma.org, Accessed 6th March 2006.

[5] Globus toolkit homepage. http://globus.org, Globus Alliance 2006.

[6] Goddard earth sciences data and information services center homepage. http://daac.gsfc.nasa.gov, National Aeronautics and Space Administration 2006.

[7] Gridftp. http://globus.org/toolkit/data/gridftp/, Globus Alliance 2006.

[8] Gt 4.0 data replication service. http://wwwunix.globus.org/toolkit/docs/4.0/techpreview/datarep/, Globus Alliance 2006.