1 research outputs found
POCS Based Super-Resolution Image Reconstruction Using an Adaptive Regularization Parameter
Crucial information barely visible to the human eye is often embedded in a
series of low-resolution images taken of the same scene. Super-resolution
enables the extraction of this information by reconstructing a single image, at
a high resolution than is present in any of the individual images. This is
particularly useful in forensic imaging, where the extraction of minute details
in an image can help to solve a crime. Super-resolution image restoration has
been one of the most important research areas in recent years which goals to
obtain a high resolution (HR) image from several low resolutions (LR) blurred,
noisy, under sampled and displaced images. Relation of the HR image and LR
images can be modeled by a linear system using a transformation matrix and
additive noise. However, a unique solution may not be available because of the
singularity of transformation matrix. To overcome this problem, POCS method has
been used. However, their performance is not good because the effect of noise
energy has been ignored. In this paper, we propose an adaptive regularization
approach based on the fact that the regularization parameter should be a linear
function of noise variance. The performance of the proposed approach has been
tested on several images and the obtained results demonstrate the superiority
of our approach compared with existing methods.Comment: 4 pages,2 fig,2 tables,Published in IJCSI International Journal of
Computer Science Issues, Vol. 8, Issue 5, No 2, September 2011 ISSN (Online):
1694-081