Electrical and Electronic Engineering, Imperial College London
Doi
Abstract
The dissertation presents several signal processing algorithms for image fusion in noisy multimodal
conditions. It introduces a novel image fusion method which performs well for image
sets heavily corrupted by noise. As opposed to current image fusion schemes, the method has
no requirements for a priori knowledge of the noise component. The image is decomposed with
Chebyshev polynomials (CP) being used as basis functions to perform fusion at feature level. The
properties of CP, namely fast convergence and smooth approximation, renders it ideal for heuristic
and indiscriminate denoising fusion tasks. Quantitative evaluation using objective fusion assessment
methods show favourable performance of the proposed scheme compared to previous efforts
on image fusion, notably in heavily corrupted images.
The approach is further improved by incorporating the advantages of CP with a state-of-the-art
fusion technique named independent component analysis (ICA), for joint-fusion processing
based on region saliency. Whilst CP fusion is robust under severe noise conditions, it is prone to
eliminating high frequency information of the images involved, thereby limiting image sharpness.
Fusion using ICA, on the other hand, performs well in transferring edges and other salient features
of the input images into the composite output. The combination of both methods, coupled with
several mathematical morphological operations in an algorithm fusion framework, is considered a
viable solution. Again, according to the quantitative metrics the results of our proposed approach
are very encouraging as far as joint fusion and denoising are concerned.
Another focus of this dissertation is on a novel metric for image fusion evaluation that is based
on texture. The conservation of background textural details is considered important in many fusion
applications as they help define the image depth and structure, which may prove crucial in
many surveillance and remote sensing applications. Our work aims to evaluate the performance of image fusion algorithms based on their ability to retain textural details from the fusion process.
This is done by utilising the gray-level co-occurrence matrix (GLCM) model to extract second-order
statistical features for the derivation of an image textural measure, which is then used to
replace the edge-based calculations in an objective-based fusion metric. Performance evaluation
on established fusion methods verifies that the proposed metric is viable, especially for multimodal
scenarios