Sunday, July 8, 2012


PSF Estimation via Gradient Domain Correlation

January 11

2012
This paper finds its application in image processing where a blurred image is reconstructed. The PSF is Point Spread Function which needs to be estimated to get the lens characteristics. But In practice, finding the true PSF is impossible, and usually an approximation of it is used, theoretically calculated or based on some experimental estimation. Here we propose PSF Estimation via Gradient Domain Correlation





PSF Estimation via Gradient Domain Correlation

ABSTRACT

This paper proposes an efficient method to estimate the point spread function (PSF) of a blurred image using image gradients spatial correlation. A patch-based image degradation model is proposed for estimating the sample covariance matrix of the gradient domain natural image. Based on the fact that the gradients of clean natural images are approximately uncorrelated to each other, we estimated the autocorrelation function of the PSF from the covariance matrix of gradient domain blurred image using the proposed patch-based image degradation model. The PSF is computed using a phase retrieval technique to remove the ambiguity introduced by the absence of the phase. Experimental results show that the proposed method significantly reduces the computational burden in PSF estimation, compared with existing methods, while giving comparable blurring kernel.

Existing system
           PSF Estimation using Sharp Edge Prediction, Godard algorithm, random noise target, coded exposure Deblurring are all proposed on this scenario but they don’t take into account the following
·        High Computational Overhead
·        In Accuracy of  Correlated Pixels
·        Ambiguity in absence of  Phase






PROPOSED SYSTEM
        In this proposed system  new algorithms for solving the problem with an optimized PSF calculation with Image gradients Spatial Correlation is used.
This algorithm takes into account that the gradients of clean natural images are approximately uncorrelated to each other.
Hence an Auto correlation Function of the PSF from the the covariance matrix of gradient domain blurred image is taken into consideration. Computaion of PSF with a phase retrieval technique to remove the ambiguity introduced by the absence of the phase
Experiments show that the proposed system gives an edge over other existing methods with reduced computational requirements and overweighs the existing system by a large margin.

Advantages over Existing Methods,
·        Reduced Computational Overhead
·        Applicable to Low powered Devices
·        Optimal Estimation in blurred images
·        Scales well with all Image scenarios








module’s IN PROJECT

IMage Handler
IMAGE ANALYSER
BLUR ESTIMATOR
PSF ESTIMATOR



SYSTEM REQUIREMENTS:
HARDWARE MINIMUM REQUIREMENTS:
PROCESSOR        :    PENTIUM IV 2.8 GHz
RAM                      :    512 MB
MONITOR             :    19”
HARD DISK         :     20 GB
CDDRIVE              :    52X

SOFTWARE REQUIREMENTS:
FRONT END                  :    C# .Net , VS 2008
FRAMEWORK USED    :    .net 2.0
OPERATING SYSTEM:    WINDOWS XP