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Learning Based Single Frame Image Super-resolution Using Fast Discrete Curvelet Coefficients
Anil A. Patil, Jyoti Singhai
Pages - 283 - 296     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
Super-resolution, Learning Method, Fast Discrete Curvelet Transform
High-resolution (HR) images play a vital role in all imaging applications as they offer more details. The images captured by the camera system are of degraded quality due to the imaging system and are low-resolution (LR) images. Image super-resolution (SR) is a process, where HR image is obtained from combining one or multiple LR images of same scene. In this paper, learning based single frame image super-resolution technique is proposed by using Fast Discrete Curvelet Transform (FDCT) coefficients. FDCT is an extension to Cartesian wavelets having anisotropic scaling with many directions and positions, which forms tight wedges. Such wedges allow FDCT to capture the smooth curves and fine edges at multiresolution level. The finer scale curvelet coefficients of LR image are learnt locally from a set of high-resolution training images. The super-resolved image is reconstructed by inverse Fast Discrete Curvelet Transform (IFDCT). This technique represents fine edges of reconstructed HR image by extrapolating the FDCT coefficients from the high-resolution training images. Experimentation based results show appropriate improvements in MSE and PSNR.
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Mr. Anil A. Patil
- India
Mr. Jyoti Singhai
- India