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Abstract:
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Super Resolution problem is an inverse problem and refers to the process
of producing a High resolution (HR) image, making use of one or more Low
Resolution (LR) observations. It includes up sampling the image, thereby,
increasing the maximum spatial frequency and removing degradations that arise
during the image capture namely aliasing and blurring.
The work presented in this thesis is based on learning based single image
super-resolution. In learning based super-resolution algorithms, a training set
or database of available HR images are used to construct the HR image of an
image captured using a LR camera. In the training set, images are stored as
patches or coefficients of feature representations like wavelet transform, DCT, etc.
Single frame image super-resolution can be used in applications where database
of HR images are available. The advantage of this method is that by skilfully
creating a database of suitable training images, one can improve the quality of the
super-resolved image.
A new super resolution method based on wavelet transform is developed and
it is better than conventional wavelet transform based methods and standard
interpolation methods. Super-resolution techniques based on skewed anisotropic
transform called directionlet transform are developed to convert a low resolution
image which is of small size into a high resolution image of large size.
Super-resolution algorithm not only increases the size, but also reduces the
degradations occurred during the process of capturing image. This method
outperforms the standard interpolation methods and the wavelet methods, both
visually and in terms of SNR values. Artifacts like aliasing and ringing effects are
also eliminated in this method. The super-resolution methods are implemented
using, both critically sampled and over sampled directionlets. The conventional
directionlet transform is computationally complex. Hence lifting scheme is used
for implementation of directionlets. The new single image super-resolution method
based on lifting scheme reduces computational complexity and thereby reduces
computation time. The quality of the super resolved image depends on the type of
wavelet basis used. A study is conducted to find the effect of different wavelets on
the single image super-resolution method. Finally this new method implemented
on grey images is extended to colour images and noisy images |