Part 1.1: Finite Difference Operator

Apply the given convolutions, and take squared sum for the edges

Part 1.2: Derivative of Gaussian (DoG) Filter

By applying a Gaussian filter first, the edges are "denoised", since the noise is washed away when adding more noise.

Part 1.3: Image Straightening

Trying angles between [-3, 3], -3 worked the best. Looking at the histograme, we want to align a peak.

Here are a few hard images that I ran the algorithm on

This one aligned with gravity and vertical, and is hard due to the branches. The feathers do give it away though, so it's rotated by 1 degree

As a failure, it's top down, so our heuristic of vertical and horizontal lines no longer works. The histogram doesn't have peaks.

So the rotation doesn't really make sense

For a well aligned image, there is no difference to align, and so no rotation is applied

Part 2.1: Image "Sharpening"

This is the result of sharpening the blurry Taj image. I simply added the high frequency component of the image once in the second panel and twice in the third panel.

Here's a cool image of sharpening this image. The left glasses is sharpened a lot, as well as the light patterns

Blurring then sharpening the bird

A lot of the background information from the bird is lost when blurred then sharpened.

Part 2.2: Hybrid Images

As a failure, if the images are not aligned properly, then the disalignment is very apparent. However on a small scale, it can't be noticed too much, so I consider it a success

Otherwise, PP (Potato Pieter) looks great! Close up and far away, it's much easier to focus on the Potato vs Pieter's eyes.

Part 2.3: Gaussian and Laplacian Stacks

Here are the stacks for Part 2.4's image.

Part 2.4: Multiresolution Blending

Here's the oraple

I tried, however the blending doesn't seem to be so good when the mask is not very good. So that's a failure case I guess