Two-dimensional Chebyshev polynomials for image fusion

Abstract

This paper presents a novel method for fusing images in a domain concerning multiple sensors and modalities. Using Chebyshev polynomials as basis functions, the image is decomposed to perform fusion at feature level. Results show favourable performance compared to previous efforts on image fusion using ICA. The work presented here aims at providing a novel framework for future studies in image analysis and may introduce innovations in the fields of surveillance, medical imaging and remote sensing.

Region-based image fusion using a combinatory Chebyshev-ICA method

“…For noisy image examples, it was noted that CP approximates the image better due to its smooth property, especially in situations with high level of noise as explored in [5]. However ICA still retains sharp edges and texture and as such is useful in scenarios where noise is minimal.…”

“…The properties of CP have been elaborated in [5]. Chebyshev polynomials can also be used to approximate a signal f (x) with…”

“…Fusion methods, and indeed signal decomposition (since fusion is just one of many extensions of signal decomposition), using Chebyshev polynomials (CP) [5] and independent component analysis (ICA) [1] have been well established in the recent literature. Nevertheless, both methods tend to have their own strong and weak points and as such, a novel approach of combining the best aspects of CP and ICA is proposed.…”

Accurate and Efficient Evaluation of Chebyshev Tensor Product Surface

“…Bivariate Chebyshev polynomials have gained attention of the computer vision researchers [5,6]. Over the years, researchers have focused on the implementation of Chebyshev tensor product series in image analysis [5][6][7]. The Chebyshev tensor product series can be used to approximate an image, which is essentially regarded as a two-dimensional spatial function [8].…”

“…Rahmalan et al [6] propose a novel approach based on discrete orthogonal Chebyshev tensor product for an efficient image compression. Recently, Omar et al [7] propose a novel method for fusing images using Chebyshev tensor product series. All above need an image reconstruction from a finite Chebyshev tensor product surface.…”