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Image Fusion is the method which conglomerates complimentary information from the source images to a single fused image . There are numerous applications of image fusion in the current scenario such as in remote sensing, medical diagnosis, machine vision system, astronomy, robotics, military units, biometrics, and surveillance. Objective: In this case multi-sensor or multi-focus devices capture images of the particular scene which are complementary in the context of information content to each other. The details from complementary images are combined through the process of fusion into a single image by applying the algorithmic formulas. The main goal of image fusion is to fetch more and proper information from the primary or source images to the fused image by minimizing the loss of details of the images and by doing so to decrease the artifacts in the final image. Methodology: In this paper, we proposed a new method to fuse the images by applying a cross bilateral filter for gray level similarities and geometric closeness of the neighboring pixels without smoothing edges. Then, the detailed images obtained by subtracting the cross bilateral filter image output from original images are being filtered through the rolling guidance filter for scale aware operation. In particular, it removes the small-scale structures while preserving the other contents of the image and successfully recovers the edges of the detailed images. Finally, the images have been fused using a weighted computed algorithm and weight normalization. Results: The results have been validated and compared with various existing state-of-the-art methods both subjectively and quantitatively. Conclusion: It was observed that the proposed method outperforms the existing methods of image fusion.

Image Fusion based on Cross Bilateral and Rolling Guidance Filter through Weight Normalization