A Novel Super Resolution Algorithm Based on Wavelet Transform and Linear Regression

  • 蘇 文伶

學生論文: Master's Thesis

摘要

The main objective of image super resolution technology is to reconstruct a high-resolution image from a low-resolution image and make the high-resolution image become clear and natural In this Thesis a new wavelet based super resolution algorithm is proposed and it is based on the idea that the low-resolution image is the low frequency subband of a higher resolution image and the high frequency subbands are estimated to reconstruct the high-resolution image To reduce the drawbacks of traditional wavelet based super resolution algorithms such as jaggy artifacts and ringing artifacts an effective directional interpolation is adopted to accurately preserve the edges Furthermore simple linear regression is used in conjunction with the relationship of a pair of low-resolution and high-resolution images to build a reconstruction model The coefficients in high frequency subbands are estimated by the reconstruction models and a high-resolution image is generated From the experimental results it is clear that the proposed algorithm provides better performance
獎項日期2016 八月 9
原文English
監督員Shen-Chuan Tai (Supervisor)

引用此

A Novel Super Resolution Algorithm Based on Wavelet Transform and Linear Regression
文伶, 蘇. (Author). 2016 八月 9

學生論文: Master's Thesis