CS 194-26
Project 3: Fun with Frequencies and Gradients!
Minjoo Sur
Part 1: Frequency Domain
1.1: Warmup
These are the images before and after sharpened. To sharpen image, first, read in an image and Low-pass filter the image using a gaussian filter. Second, use the low-pass filtered image to extract the high-pass signals from original image. Last, add the high-pass signals to the original image.
Original
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alpha = 1
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alpha = 2
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alpha = 5
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1.2 Hybrid Images : Hybrid images and the Fourier analysis
Using Hybrid Images as described in the paper by Oliva et al, we layer the high frequencies from one image on top of low frequencies of another. With proper sigma values, the hybrid image can be very natural.
Derek
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Nutmeg
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gray hybrid
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color hybrid
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Minjoo(Myself)
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Timothy(My Boyfriend)
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Failed Timinjoo
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Successful Timinjoo
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Minjoo FFT
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Timothy FFT
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Minjoo Low Pass FFT
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Tim High Pass FFT
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Timinjoo FFT
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Below are the one which didn't work that well....
Tiger
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Camel
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Just Tiger...?
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1.3: Gaussian and Laplacian Stacks
The pictures below are examples of the gaussian and laplacian stacks at different levels. Unlike pyramids, stacks does not downsample. Gaussian filters smooth the image and Laplacian images bring out the sharper edges. By combining gaussian and laplacian of two images, we were able to generate hybrid images in the last part.
1.4: Multiresolution Blending
In this part, we perform multiresolution blending to produce a gentle seam between two images separately at each band of image frequencies. This results in a much smoother seam that gently blurs one image into another.
Turned Off
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Turned On
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Both
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Part 2: Gradient Domain Fusion
Our eyes are sensitive much more about the gradient of an image than the overall intensity. So, when blending, we want the gradient of the composite inside the region to look as close as possible to the source image gradient, and the composite must match the target image on the boundary.
2.1: Toy Example
For toy problem, we reduce the least square problem into a linear equation. We also use Poisson blending. The general idea is that for every corresponding pixels, the diffence between their neighbors should be the same.
Toy Before
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Toy After
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2.2: Poisson Example
Source
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Target
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Raw Blend
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Poisson Blend
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Source
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Target
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Raw Blend
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Poisson Blend
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