A Comparative Analysis of Transforms for Infrared and Visible Image Fusion

Image fusion is the art of combining two different images which are either captured on different times, using different sensors, from different focal points or from different modalities to fuse the best available within two into single one. The fusion of infrared and visible images has a widespread application in the field of military surveillance and night vision imaging technologies. The era of evolution of various transforms has led to the documentation of various efficient representational algorithms in literature, for instance, Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) for the fusion of images. It is clearly stated in the field of image fusion that high quality of source images largely affects the image fusion rate. Therefore, in this paper, we explore and compare various transform-based image fusion techniques for noisy visible and infrared images.

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