High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Newton Rapson Regression (NRR) Based Image Interpolation Methods: A Review


Shubhra Pal , Gyan Ganga College of Technology, Jabalpur; Prof. Neeta Nathani, Gyan Ganga College of Technology, Jabalpur


Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Percentage Edge Error (PEE), Magnification Factor (MF), Nearest Nabors (NN)


As the size of an image is enlarged, the pixels that form the image become increasingly visible, making the image appear "soft" if pixels are averaged, or jagged if not. Image interpolation methods however, often suffer from high computational costs and unnatural texture interpolation. The proposed work proposes a unique edge-adaptive image interpolation method using an edge-directed smoothness filter. Many image interpolation techniques are already been developed and designed we are proposing a new method is been used for edge-adaptive image interpolation which uses Newton forward difference. This difference provides very good grouping of pixels ones we consider target pixel for interpolation Proposed approach estimates the enlarged image from the original image based on an observation model. Simulation results for the work will can get by MATLAB and expecting that for the proposal method it will produces images with higher visual quality, higher PSNRs and faster computational times than the conventional methods.

Other Details

Paper ID: IJSRDV6I80162
Published in: Volume : 6, Issue : 8
Publication Date: 01/11/2018
Page(s): 211-213

Article Preview

Download Article