Image Interpolation Using
High-Resolution Cubic Spline Curves
Ranjeet Kumar Roy and Tarun
Department of Electronics & Communication Engineering, Maharishi Markandeshwar University, Mullana (Ambala), INDIA
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.
Quantization, Sampling, & Interpolation.
eJournal of Mathematical Sciences, Technology and Humanities
Issue 2, Pages: 1063 - 1069