Image Fusion for Context
Enhancement and Video Surrealism
Group No. - 10
Kumar Srijan(200602015)
Siddharth Choudhary(200601088)
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).
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.
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
2
∇ I’= div G.
• We will get one equation for each pixel of the output image.
• This can be represented graphically as:
This shows the system of equations for a 4*4 image
Implementation Details
• We are using Matlab for coding
• We are using the Finite Element Method for
solving the system of equations.
Difficulties faced
• Deciding the Importance image.
• Solving the huge system of equations.
• Dealing with the boundary conditions for the
Poisson equations.
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.
Input day image
Input Night image
Importance image
Gradient Image in X-direction
Gradient Image in X-direction
Context Enhanced Image
Observations
• There are slight color shifts in the output
image.
Interpretation
• Night time image has been visually enhanced.
• Blending of the images in gradient domain
reduces artifacts like ghosting and haloing.
Deliverables completed
• Image fusion for context enhancement of
static images has been done to a large extent.
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.