Performance Of Image Fusion Algorithms Research Articles

Comprehensive measure for evaluating image fusion algorithm

Evaluating the performance of image fusion algorithms objectively is still an open problem. We proposed an integrational approach to objective image fusion evaluation based on integrating different existing objective metrics in order to overcome their individual shortcomings. The process of the objective measure construction contains two steps: first, we construct a candidate measure set, where each element contains high evaluation accuracy and low correlation with each other; then, the weights of measures in constructing the target measure are determined dynamically on training image set. Considering that the true quality of fused images is a subjective perception, we introduce assessment indefiniteness when calculating the evaluation accuracy of each measure to reduce the influence of errors of subjective perception. Experiments on various images are conducted to test the effectiveness of the proposed measure. The results have shown that the measure is useful in conjunction with the other objective measures to account for qualitative subjective perception.

Theoretical analysis of correlation-based quality measures for weighted averaging image fusion

Recently introduced correlation-based quality measures have received lots of attention due to the fact that they do not need ground-truth reference images to evaluate the performance of image fusion algorithms. In this paper we focus on theoretical analysis of these correlation-based quality measures when they are used to judge the performance of weighted averaging image fusion algorithms. The purpose of this paper is to rigorously prove that the correlation-based quality measures have some undesired behavior under certain conditions. We employ a statistical model for the observed sensor images and study the properties of these correlation-based quality measures. Our analysis shows that when we change the power of the desired signal or the noise in the input images, these correlation-based quality measures exhibit bad behaviors in some cases, indicating higher quality when lower quality is evident. The sufficient conditions for when the undesired behavior occur and the intuitive explanation for our observation are given in this paper. Investigations with real images also demonstrate the utility of the theoretical analysis, by illustrating its predictive capabilities.

English

Image registration and fusion are of great importance in defence and civilian sectors, e.g. , recognising a ground/air force vehicle and medical imaging. Pixel-level image fusion using wavelets and principal13; component analysis has been implemented and demonstrated in PC MATLAB. Different performance metrics with and without reference image are implemented to evaluate the performance of image fusion algorithms. As expected, the simple averaging fusion algorithm shows degraded13; performance. The ringing tone presented in the fused image can be avoided using wavelets with shift invariant property. It has been concluded that image fusion using wavelets with higher level of decomposition showed better performance in some metrics and in other metrics, principal components analysis showed better performance.13;