Emission characteristics and dynamics of C2 from laser produced graphite plasma

The emission features of laser ablated graphite plume generated in a helium ambient atmosphere have been investigated with time and space resolved plasma diagnostic technique. Time resolved optical emission spectroscopy is employed to reveal the velocity distribution of different species ejected during ablation. At lower values of laser fluences only a slowly propagating component of C2 is seen. At high fluences emission from C2 shows a twin peak distribution in time. The formation of an emission peak with diminished time delay giving an energetic peak at higher laser fluences is attributed to many body recombination. It is also observed that these double peaks get modified into triple peak time of flight distribution at distances greater than 16 mm from the target. The occurrence of multiple peaks in the C2 emission is mainly due to the delays caused from the different formation mechanism of C2 species. The velocity distribution of the faster peak exhibits an oscillating character with distance from the target surface.

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

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