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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