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Thermal Images profile the passive radiation of objects and capture them ingrayscale images. Such images have a very different distribution of datacompared to optical colored images. We present here a work that produces agrayscale thermo-optical fused mask given a thermal input. This is a deeplearning based pioneering work since to the best of our knowledge, there existsno other work on thermal-optical grayscale fusion. Our method is also unique inthe sense that the deep learning method we are proposing here works on theDiscrete Wavelet Transform (DWT) domain instead of the gray level domain. As apart of this work, we also present a new and unique database for obtaining theregion of interest in thermal images based on an existing thermal visual paireddatabase, containing the Region of Interest on 5 different classes of data.Finally, we are proposing a simple low cost overhead statistical measure foridentifying the region of interest in the fused images, which we call as theRegion of Fusion (RoF). Experiments on the database show encouraging results inidentifying the region of interest in the fused images. We also show that theycan be processed better in the mixed form rather than with only thermal images.

A Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images