Image Fusion by Osamu Ukimura (ed.)

By Osamu Ukimura (ed.)

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15. , face image. In Fig. 15, S+sick is the face image of the patient and M+sick is the corresponding morphed image. In this case, doctor B may examine the sickness of the Image Enhancement and Image Hiding Based on Linear Image Fusion 39 patient without knowing who he/she is. This is impossible for conventional steganographic approaches. More details are given in the following. Fig. 15. Scenario to share sickness information while hiding the patient’s face image Fig. 16 shows the morphed images of a patient with different morphing rates.

The implementation steps of de-morphing to recontruct the source image are given as follows. Step 1. Input the warped target image I tw . Step 2. Obtain the warped source image I sw as I sw = [ I m - (1 - α)I tw ] / α (5) Step 3. Calculate the skeleton of the source image as Fs = [ Fm - ( 1 - α ) Ft ] / α (6) I s = dewarp( I sw , Fm , Fs ) (7) Step 4. ) is a function to de-warp the source image. Fig. 12 shows an example of de-morphing. As described previously, the source image can be recontructed almost perfectly except the borders.

11 the intermediate images, especially those close to the center, can be used to hide the source (or target) image. That is, image hiding can be achieved by morphing technology based on LIF. Fig. 11. An example of image morphing Image Enhancement and Image Hiding Based on Linear Image Fusion 35 To reconstructed the source (or target) image from the intermediate image, the target (or the source) image, the skeletons, and the morphing rate are required. The process to reconstruct the source (or target) image is called de-morphing, that is, inverse of morphing.

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