diff --git a/src/pages/science/super-resolution.md b/src/pages/science/super-resolution.md index 00e6c6a5..da03b183 100644 --- a/src/pages/science/super-resolution.md +++ b/src/pages/science/super-resolution.md @@ -26,7 +26,14 @@ For this project, I explored two different types of models that have played a re - A method that outperforms GAN models in rendering quality - A type of Generator - Adds and removes noise from the original image at a slower rate - - Better method than the GAN Model generator since it gives the model time to learn complex patterns + - Better method than the GAN Model generator since it gives the model additional time to learn complex patterns + - Main drawback is the time complexity will be longer + - Process is similar to a thermodynamics problem + - Analogy for adding noise: + - Adding a drop of food coloring into a large bowl of water has a very high probability of the food coloring affecting the other water droplets + - Results in all the water droplets being affected by food coloring + - Analogy for removing noise: + - Slowly rewinding time and pinpointing the exact location of where the initial drop of food coloring came from starting from when the bowl is filled with food coloring water droplets - Adjusts loss to make sure the image is as high quality as possible