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and Voila! hi-res image. Impossible right? | and Voila! hi-res image. Impossible right? | ||
| − | After 6 hours of coding, and processing the image of my dog "Milo", countless times, I am proud to say, that it is | + | After 6 hours of coding, and processing the image of my dog "Milo"(shown below), countless times, I am proud to say, that it is |
"Almost Possible in the Real World" | "Almost Possible in the Real World" | ||
[[Image:Example1.jpg]] | [[Image:Example1.jpg]] | ||
---- | ---- | ||
| + | |||
==Page Map== | ==Page Map== | ||
---- | ---- | ||
This page contains | This page contains | ||
| − | **A mathematical basis for discrete time interpolation | + | |
| + | ** A mathematical basis for discrete time interpolation | ||
| + | ** Application to a simple 1-D signal | ||
| + | ** Application to an actual real-world image | ||
| + | |||
| + | |||
| + | ---- | ||
Revision as of 23:25, 22 September 2009
Discrete Time Interpolation
Mathematically, sure. Realistically? Let's find out.
== Introduction ==
My fascination with the concept of discrete time interpolation began, when I asked Prof. Boutin if a discrete time interpolator, can ideally make a low-resolution image, a high resolution one.
The answer was, yes. Ideally, it can.
The equations that led to the concept seemed impeccable and mathematically, it seemed to make perfect sense.
But essentially, all we are doing is:
- adding zeros in between samples (the result of which looks horrible by the way)
- Low pass filtering,
and Voila! hi-res image. Impossible right?
After 6 hours of coding, and processing the image of my dog "Milo"(shown below), countless times, I am proud to say, that it is
"Almost Possible in the Real World"
Page Map
This page contains
- A mathematical basis for discrete time interpolation
- Application to a simple 1-D signal
- Application to an actual real-world image
