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Image Interpolation Using High-Resolution Cubic Spline Curves

Ranjeet Kumar Roy and Tarun Gulati

Department of Electronics & Communication Engineering, Maharishi Markandeshwar University, Mullana (Ambala), INDIA

Email: ranjeetroy208@gmail.com  & gulati_tarun@rediffmail.com

ABSTRACT

Image processing for low resolution digital images (e.g.low resolution camera and computed tomography (CT) scan medical images) is very challenging problems. It is because of the errors due to quantization and sampling. Over the last several years; significant improvements have been made in this area; however, it is still very challenging. Therefore, this paper focuses on investigating the effect of interpolation functions on using high-resolution cubic spline functions. For this purpose, ideally, an ideal low-pass filter is preferred; however, it is difficult to realize in practice. Therefore, four interpolation functions (nearest neighbor, linear, cubic B-spline and high-resolution cubic spline (-2≤a≤0). From the results, it is found that cubic B-spline and high-resolution cubic spline have a better frequency response than nearest neighbor and linear interpolation functions.

Keywords: Pixel, Quantization, Sampling, & Interpolation.

 

 

 

 

 

International eJournal of Mathematical Sciences, Technology and Humanities

Volume 3, Issue 2, Pages:  1063 - 1069