1. Gaussian and the Laplacian pyramids
2. Use of Pyramids to encode the Frequency domain
3. Compute a laplacian pyramid form a Gaussian Pyramid
4. Blend two images using pyramids
avoid seams: Window = size of largest prominent “feature”
avoid ghosting window <= x size of smallest prominent "feature"
use Fourier domain
largest frequency <= 2 * size of smallest frequency
image frequency content should occupy one octave(power of two)
Frequency spread needs to be modeled
compute FFT(Ij) => Fl, FFT(Ir) => Fr
Decompose Fourier image into octaves(bands)
“Feather” corresponding octaves of Fl Fr
Compute inverse FFT and feather in spatial domain
sum feathered octave images in frequency domain
Pryamid Representation: A Gaussian Pyramid
a = 0.3 – 0.6(.38)
h = wh * wv
gk = h * g(k-1)
gk = REDUCE(g(k-1))
L1 = g1 – EXPAND(g1+1)
A series of “error” images
A difference between two levels of a Gaussian Pyramid
