원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.7 No.1
2014.02
pp.283-298
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
The High resolution (HR) images can be obtained from a set of noisy and blurred low resolution (LR) observations by applying the Super Resolution (SR) technique. In this paper a new SR algorithm that uses Singular Value Decomposition (SVD) based Fusion and Blind deconvolution techniques is proposed. The algorithm significantly improves the resolution and eliminates the noise and blur associated with low resolution images, when compared with the other existing methods.
목차
Abstract
1. Introduction
2. SR Methods based on the Type of Fusion
2.1. Average Fusion Based SR:
2.2. Principle Component Analysis (PCA) Fusion Based SR:
2.3. Discrete Wavelet Transform (DWT) Fusion based SR:
2.4. Scale Invariant DWT(SIDWT) Fusion Based SR:
2.5. Radon Transform Fusion based SR:
2.6. Image Pyramid Approaches:
3. Proposed Algorithm: SVD Fusion Based SR
3.1. Automatic Feature Based Registration Using SIFT:
3.2. Singular Value Decomposition (SVD) Fusion:
3.3. Bicubic Interpolation:
3.4. Blind De-Convolution Restoration:
4. Results and Discussion
5. Conclusions
References
1. Introduction
2. SR Methods based on the Type of Fusion
2.1. Average Fusion Based SR:
2.2. Principle Component Analysis (PCA) Fusion Based SR:
2.3. Discrete Wavelet Transform (DWT) Fusion based SR:
2.4. Scale Invariant DWT(SIDWT) Fusion Based SR:
2.5. Radon Transform Fusion based SR:
2.6. Image Pyramid Approaches:
3. Proposed Algorithm: SVD Fusion Based SR
3.1. Automatic Feature Based Registration Using SIFT:
3.2. Singular Value Decomposition (SVD) Fusion:
3.3. Bicubic Interpolation:
3.4. Blind De-Convolution Restoration:
4. Results and Discussion
5. Conclusions
References
저자정보
참고문헌
자료제공 : 네이버학술정보