A Review of Quality Metrics for Fused Image

Image fusion is the process of combining high spatial resolution panchromatic (PAN) image and rich multispectral (MS) image into a single image. The fused single image obtained is known to be spatially and spectrally enhanced compared to the raw input images. In recent years, many image fusion techniques such as principal component analysis, intensity hue saturation, brovey transforms and multi-scale transforms, etc., have been proposed to fuse the PAN and MS images effectively. However, it is important to assess the quality of the fused image before using it for various applications of remote sensing. In order to evaluate the quality of the fused image, many researchers have proposed different quality metrics in terms of both qualitative and quantitative analyses. Qualitative analysis determines the performance of the fused image by visual comparison between the fused image and raw input images. On the other hand, quantitative analysis determines the performance of the fused image by two variants such as with reference image and without reference image. When the reference image is available, the performance of fused image is evaluated using the metrics such as root mean square error, mean bias, mutual information, etc. When the reference image is not available the performance of fused image is evaluated using the metrics such as standard deviation, entropy, etc. The paper reviews the various quality metrics available in the literature, for assessing the quality of fused image. (C) 2015 The Authors. Published by Elsevier B.V.