CS 194-26 Project 2

Dylan Tran

1.1

For this part, we took the camerman image and computed gradients in each direction. To compute the gradient magntiude, we computed sqrt(gradient_x^2 + gradient_y^2). This allows us to visualize the edge strength or the direction of greatest increase.

Results

1.2

This time, we blur using a gaussian filter. This greatly reduced the noise as compared to 1.1. The edges are cleaner and more defined.

Results - blur first then gradient

Results - dog filter

2.1

In this part, we sharpened images by taking the high frequency components of an image and readding it to the image. We do this by removing blurry parts with a gaussian filter to get only the high frequencies. We combined this operation into 1 convolution by creating an unsharp mask filter.

Results - Sharpening

2.2

In this part, we overlayed and combined two images by taking the lower frequency components of 1 image and averaging it with the high frequency components of another. The overlay of hilfinger and denero did not work so well. The shoulders were misaligned and their hair style is different. Additionally, denero was smiling with his teeth while hilfinger was not.

Results - Hybrid

2.3 & 2.4

In this part, we computed laplacian and gaussian stacks to do multiresolution blending

Visualizing stacks (figure 3.42)

Results - Blend