Image and Video Bending

Last week's activity was interesting -- we restored the color in old images digitally! This was interesting because finally I understood what the fancy jargon that came with it meant already. Apparently there are different ways to represent color in images and the one that we played around with were with the RGB and HSV representation of color in images. RGB represents the amount of red, green, and blue there is in an image [1] which is practical, because they are the primary colors of light [1] from which other colors may be made from. This kind of representation however, isn't practical for heavy color restoration because we do not describe colors like this. Rather we describe them through their hue, saturation, and lightness/value [1]. Thus we use HSV to do just that. Hue and saturation represents the purity of color and how much it is diluted by white light [1], and value is how far the color is from black, which may simply be its gray-level. In this activity, we mess...

Before I begin, I'd like to say that I was hoping this activity to be very fun -- but it was fun in a not-so-good way. To put it simply, it was just very long, and a lot had to be done just to finish one part! I'm just glad I'm already writing this one down so that I don't have to think about it and move on to the next 'short' activity. The activity was split into six parts. In the first part, we were tasked to take the Fourier transform of: a wide, and tall rectangle, and dots placed symmetrically and asymmetrically with respect to the image center. After doing so, we get Figure 1. Figure 1: Fourier Transform of tall and wide rectangle, and dots placed symmetrically and asymmetrically with respect to the image center. Looking at the figure we see that, as expected, the Fourier transform of either rectangles give out a pattern similar to the diffraction pattern through a single slit, which further was asserted in the previous post that Fourier trans...