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PPT FRAMELET | what is Framelet Algorithms for De-Blurring Images Corrupted by Impulse Plus Gaussian Noise|image processing

9:45 PM - By Reetha 1


1.PPT | what is Framelet Algorithms for De-Blurring Images Corrupted by Impulse Plus Gaussian Noise|image processing        

ABSTRACT:

This paper studies a problem of image restoration that observed images are contaminated by Gaussian and impulse noise. Existing methods for this problem in the literature are based on minimizing an objective functional having the l1 fidelity term and the Mumford-Shah regularizer. We present an algorithm on this problem by minimizing a new objective functional. The proposed functional has a content-dependent fidelity term which assimilates the strength of fidelity terms measured by the l1 and l2 norms. The regularizer in the functional is formed by the l1 norm of tight framelet coefficients of the underlying image. The selected tight framelet filters are able to extract geometric features of images. We then propose an iterative framelet-based approximation/sparsity deblurring algorithm (IFASDA) for the proposed functional. Parameters in IFASDA are adaptively varying at each iteration and are determined automatically. In this sense, IFASDA is a parameter-free algorithm. This advantage makes the algorithm more attractive and practical. The effectiveness of IFASDA is experimentally illustrated on problems of image deblurring with Gaussian and impulse noise. Improvements in both PSNR and visual quality of IFASDA over a typical existing method are demonstrated. In addition, Fast_IFASDA, an accelerated algorithm of IFASDA, is also developed.

2.FRAMELET ALGORITHM FOR ENHANCING VIDEO STILLS

Abstract.


 High-resolution image reconstruction refers to the problem of constructing a high resolution
image from low resolution images. One approach for the problem is the recent framelet
method in [4]. There the low resolution images are assumed to be small perturbation of a reference
image perturbed in di erent directions. Video clips are made of many still frames, usually about
30 frames per second. Thus most of the frames can be considered as small perturbations of their
nearby frames. In particular, frames close to a speci ed reference frame can be considered as small
perturbations of the reference frame. Hence the setting is similar to that in high-resolution image
reconstruction. In this paper, we propose a framelet algorithm similar to that in [4] to enhance the
resolution of any speci ed reference frames in video clips. Experiments on actual video clips show
that our method can provide information that are not discernable from the given clips.


 

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1 comment:

  1. if you want any ieee paper or ieee base paper please ping us we will send you :)

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