1. Image Fusion for Context Enhancement and Video Surrealism
Group No. - 10
Kumar Srijan( )
Siddharth Choudhary( )

2. Technical Details
Task is to add context to low contrast night time images using high contrast day time images.
Naïve approaches like simple cutting and pasting of pixels from day time images or averaging will leave artifacts like ghosting , haloing etc.
We will compute gradient of the two images(I1,I2).
Gradient is computed as first difference in both X and Y direction.
Using the gradient of the night image we can compute the importance function(W).

4. Technical details(cont..)
Importance image shows which parts of the night time image contain information.
It can change on application to application.
We are using a binary importance image .
To compute the Importance image, we threshold the gradient of the night time image.
Using the gradients of the day time and night time images and the Importance Image, we can compute the mixed gradient image(G).
Now we have to use this mixed gradient image to construct the final output image.

5. Technical Details Image Reconstruction from gradient field
Approximately an invertibility problem, so solution is not so trivial.
In 2D, a modified gradient vector field G may not be integrable.
So we try to minimize |∇I’− G|, where I’ is the final image.
This can be done by solving the Poisson differential equation ∇ I’= div G.
We will get one equation for each pixel of the output image.
This can be represented graphically as: 2

6. This shows the system of equations for a 4*4 image
U is the final image and b is the gradient image.

7. Implementation Details
We are using Matlab for coding
We are using the Finite Element Method for solving the system of equations.

8. Difficulties faced Deciding the Importance image.
Solving the huge system of equations.
Dealing with the boundary conditions for the Poisson equations.

9. Results Obtained Computed the importance images for night time images.
Found a way to solve Poisson equation.
Got an approximate blending of day and night images to get the context enhanced image with slight color shifts.

10. Input day image

11. Input Night image

12. Importance image

13. Gradient Image in X-direction

14. Gradient Image in X-direction

15. Context Enhanced Image

16. Observations
There are slight color shifts in the output image.

17. Interpretation Night time image has been visually enhanced.
Blending of the images in gradient domain reduces artifacts like ghosting and haloing.

18. Deliverables completed
Image fusion for context enhancement of static images has been done to a large extent.

19. What remains to be done
Automation of the Computation of the importance image.
Extending this concept to find a way to enhance night time videos.