Technical Approach
These are some of the resulting images.
There are basically four steps in the processing:
1. Cropping the image: I crop 1/10 of the image in every side
2. Resizing the image (similar to image pyramid): downscaling the image (usually I use a factor of 0.25) to reduce searching time If I am calculating over a small image which doesn't need downsizing, I would set the factor to be equal to 1.
3. Searching for the displacement: Performing exhaustive search over a window. I usually set the window to be [-1/10 of the image width, 1/10 of the image width]. In this window, I calculate the SSD of all pixels over two channels, and use the one with the least SSD as the displacement vector. If the displacement vector that I got is equal to the borders of the window, I would extend the window to [-1/(n-1) of the image width, 1/(n-1) of the image width] until the displacement vector is within the searching window.
4. Outputing the final result: roll the channels according to the displacement vector*(1/downscale factor) calculated from step 3, stack the three channels and output image.
For Emir, the problem is that the brightness of the three color channels aren't the same.
Displacement vectors
cathedral.jpg: G(20, 8) R(48, 12)
church.tif: G(24, 4) R(56, -4)
emir.tif: G(48, 24) R(104, 56)
harvesters.tif: G(60, 16) R(124, 12)
icon.tif: G(40, 16) R(88, 24)
lady.tif: G(52, 8) R(112, 12)
melons.tif: G(80, 8) R(180, 12)
monastery.jpg: G(-12, 8) R(12, 8)
onion_church.tif: G(52, 28) R(108, 36)
self_portrait.tif: G(80, 28) R(176, 36)
three_generations.tif: G(52, 12) R(112, 12)
tobolsk.jpg: G(12, 12) R(24, 12)
train.tif: G(44, 4) R(88, 32)
workshop.tif: G(52, 0) R(104, -12)