Intensity-curvature functional based digital high pass filter of the bivariate cubic B-spline model polynomial
Carlo Ciulla1, Grace Agyapong2
1Faculty of Information Systems, Visualization, Multimedia, and Animation, University of Information Science and Technology, St. Paul the Apostle, Partizanska B.B., Ohrid, 6000, Republic of North Macedonia. cxc2728@njit.edu.
Visual Computing for Industry, Biomedicine, and Art
|April 3, 2020
Summary
This study introduces an intensity-curvature functional (ICF) based high-pass filter (HPF) designed using bivariate cubic B-splines. ICF-based HPF shows promise, though traditional HPFs yield sharper images.
Area of Science:
- Digital Image Processing
- Computational Imaging
- Applied Mathematics
Background:
- High-pass filters (HPFs) are crucial for image enhancement.
- Existing HPF designs often lack theoretical grounding in curvature concepts.
- The intensity-curvature concept offers a novel approach to signal processing.
Purpose of the Study:
- To design and mathematically describe an intensity-curvature functional (ICF) based digital high-pass filter (HPF).
- To validate the hypothesis that ICF acts as an HPF signal.
- To compare the performance of the ICF-based HPF against traditional and PSO-based HPFs.
Main Methods:
- Calculation of ICF using a bivariate cubic B-spline model.
- Development of the intensity-curvature concept for 2D image analysis.
- Empirical comparison of ICF-based HPF with ten other HPFs, including traditional and particle swarm optimization (PSO) based methods, in image and k-space.
Main Results:
- The theoretical basis for ICF as an HPF signal was established.
- Empirical validation confirmed ICF's HPF capability.
- Both traditional HPF and ICF-based HPF outperformed PSO-based filtering.
- Traditional HPF resulted in sharper images compared to ICF-based HPF.
Conclusions:
- A mathematical framework for designing 2D HPFs from bivariate polynomial functions using the ICF concept was confirmed.
- The ICF-based HPF is a viable alternative, though traditional HPFs offer superior sharpness.
- Further research can explore optimizing ICF-based filters for enhanced image sharpness.
Related Concept Videos
Introduction to Polynomial Functions
154
Polynomial functions are fundamental elements in algebra and calculus, defined by expressions that combine variables and constants through addition, subtraction, and multiplication, with the variable raised to nonnegative integer exponents. A general polynomial function of degree n is given byWhere an ≠ 0. The term anxn is the leading term, and an is the leading coefficient, while a0 is referred to as the constant term.Characteristics and ClassificationPolynomials are categorized by their...
154
Reconstruction of Signal using Interpolation
630
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
630
Linear Approximation in Frequency Domain
308
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
308
Basic signals of Fourier Transform
820
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
820
Tangent to a Curve
180
The graph of a function where each output is the square of the input creates a smooth curve that bends upward, becoming steeper as one moves further from the center. At any chosen position along this curve, the curve reaches a certain height depending on the input value. This position can be a reference for analyzing how the curve behaves in its immediate vicinity.To understand the change in the curve near a particular position, imagine selecting another point slightly ahead along the curve.
180
Passive Filters
898
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
898


