Blind Deconvolution Using Conjugate Gradient Based Minimization Approach

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  June 20, 2022

Blind Deconvolution Using Conjugate Gradient Based Minimization Approach

Chapter 8 CONCLUSION AND FUTURE WORK

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The job of reconstructing blurry images has motivated us to choose the well developed mathematical methods and techniques in order to obtain an estimate of the original image. To reconstruct the image which is blurred by additive gesture we are utilizing a computational method.

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To work out the additive systems of equations we have seen different types of direct and iterative methods. By probe it is seen that to work out the additive systems of equations the most efficient method is Conjugate Gradient method. To compared to other direct and iterative methods Conjugate Gradient requires less computation clip to meet.

In the method we came to cognize that the declaration of the restored image remains at really high degree and ISNR is well high. Compared to other methods computational efficiency is improved to some extent. By comparing our method with other methods we have presented our consequences.

In future work, to work out additive systems of equations some other methods can be invented. Other future waies are to look into simple and efficient method.for work outing non-linear equations.

Mentions

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Blind Deconvolution Using Conjugate Gradient Based Minimization Approach

Dept of ECE, TOCE M.Tech ( VLSI design & A ; Embedded Systems ) Page 45

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Blind Deconvolution Using Conjugate Gradient Based Minimization Approach

Dept of ECE, TOCE M.Tech ( VLSI design & A ; Embedded Systems ) Page 46

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Blind Deconvolution Using Conjugate Gradient Based Minimization Approach

Dept of ECE, TOCE M.Tech ( VLSI design & A ; Embedded Systems ) Page 48

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Blind Deconvolution Using Conjugate Gradient Based Minimization Approach

Dept of ECE, TOCE M.Tech ( VLSI design & A ; Embedded Systems ) Page 49

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