FRAMELET-BASED BLIND MOTION DEBLURRING FROM A SINGLE IMAGE

FRAMELET-BASED BLIND MOTION DEBLURRING FROM A SINGLE IMAGE

How to recover a clear image from a single motion blurred image has long been a challenging open problem in digital imaging. In this paper, we focus on how to recover a motion blurred image due to camera shake. A regularization-based approach is proposed to remove motion blurring from the image by regularizing the sparsity of both the original image and the motion- blur kernel under tight wavelet frame systems. Furthermore, an adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problem. The experiments on both synthesized images and real images show that our algorithm can effectively remove complex motion blurring from natural images without requiring any prior information of the motion- blur kernel.

Existing System:

There have been extensive research works on single-image blind deconvolution. Early works on blind deblurring usually use a single image and assume a prior parametric form of the blur kernel, such as the linear motion-blur kernel model.
These parametric motion-blur kernel models can be obtained by estimating only a few parameters, but they are often overly simplified for practical motion blurring. To remove more general motion blurring from images, some probabilistic priors on natural images’ edge distributions have

Proposed System:
We propose a new optimization approach to remove complex motion blurring from a single image by introducing new sparsity-based regularization terms on both images and motion-blur kernels. Our approach is closely related to recent works on both nonblind image deconvolution and blind motion deblurring.
Two nonblind image deconvolution algorithms in and are both based on the observation that images usually have sparse representations or approximations in some redundant transform domain, e.g., wavelet and framelet transforms. Given the blur kernel, is solved in and by seeking a sparse solution in the corresponding transformed domain.

Software Requirements:
.Net
Front End – ASP.Net
Language – C#.Net
Back End – SQL Server
Windows XP
Hardware Requirements:
RAM : 512 Mb
Hard Disk : 80 Gb
Processor : Pentium IV
FUTURE WORK:

Recently, there have been some blind deconvolution techniques that use either variation Bayesian approach or cross-validation techniques to automatically determine the optimal parameter values. In the future, we would also like to investigate how to incorporate these techniques into our method to automatically infer the optimal parameter setting of the deblurring algorithm.

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