Part 1.1: Image Sharpening
The easiest way to sharpen an image is to extract the high frequencies by applying a Gaussian filter, then subtracting this from the original image: HF = Image - GaussianFilter(Image). Then, add the high frequencies back to the image, with varying degrees: Img' = Img + α*HF.
Below, you can see the original image, as well as how the alpha value changes how much the image is sharpened.
Original Image
Sharpened, α=0.5
Sharpened, α=1
Hybrid Images are images in which you see one image when looking at the image at a close distance, and a different image when staring from far away. This image is produced when you combine the high frequencies of one image with the low frequencies of another. Since we focus on the high frequencies when an image is close to us, and we focus on the low frequencies when an image is far away, we are able to see two different images at two distances from the image
Anant Sahai
Dan Garcia
Hybrid Image (greyscaled)
Sahai FFT
Sahai, Blurred, FFT. High frequencies are reduced
Garcia FFT (greyscaled)
Garcia, Impulse, FFT. High frequencies dominate
Hybrid Image FFT
Other Results!
Rhino
Cheetah
Hybrid Image (greyscaled)
Rhino
Cheetah
Hybrid Image (greyscaled) Image manually rotated and cropped
Failures!
Sahai+Garcia Fail–needed to tweak the sigma values
Rhino+Cheetah Fail–needed to tweak the sigma values
Colors!
Gahai
Rheta
Garcat (Rotated and Cropped Manually)
Part 1.4: Multiresolution Blending
One way to blend 2 images is to blend at each frequency level
The Orange + Apple (Naive)
The Orapple!
Jupiter + Neptune (Naive)
New planet Jupitune!
Laplacian Levels of Combined Image
Level 0
Level 1
Level 2
Level 3
Level 4
The Hand + Eye (Naive Blending)
A scary hand-eye!
Part 2.1: The Toy Problem
Before we start on gradient fusions, we should get used to working in the gradient domain! We'll see how accurate our process is by using gradients and least squares to reconstruct an image.
As you can see, the reconstructed image is fairly similar to the original! We do see some artifacts in the reconstructed image, but this is expected.
Original Image
Reconstructed Iamge
Part 2.2: Poisson Blending
We're now ready to use gradients to blend!
City
City, Mask
Eagle
Eagle, Mask
Eagle + City (Naive)
Eagle flying over a city!
Skiing + Polar Bear (Naive)
Skiing with a Polar Bear
Skiing + Polar Bear, using Multiresolution Blending
The Multiresolution Blending option looks very weird. It's not able to change the pixel values, so the pixels around the polar bear are blurred out. Unless the background is very similar, multiresolution blending will be not as good. I think that Poisson blending is better if you have a small mask, and you're trying to insert a object into another image seamlessly. Multiresolution Blending is better if you're okay with fuzziness, and if you're given an expectation that the image isn't real.
Moon
Eiffel Tower
Eiffel with a detailed moon!
Failure
Cheetah + Rhino (Naive)
Cheetah weirdly part of a rhino scene
This likely didn't work because of the huge difference in the background of the two images–it resolved the tan desert + tan cheetah by turning the whole thing green, since the graident was low at the edge of the cheetah. The backgrounds need to be somewhat similar in order for colors to stay the same.