# Iterative Super Resolution

Instead of implementing the matrix-based algorithm as described in
[MEAF97], I opted for a simpler iterative approach. The matrix
approach requires knowledge of three entities:

- F: A warp matrix
- G: A blur matrix
- E: A noise vector

(verify this)

The iterative approach is simpler, and makes the following
assumptions:

- Any frame can be obtained by applying a Euclidian geometrical
transform to the frame of reference.
- There is no noise (to be addressed later, possibly using non-linear
methods).
- There is no blur.

This allows us to create a very simple super-resoltuion algorithm.
For every pixel in the reference frame:

- "Ask" each of the other frames: what should the value of this pixel
be? This requires the use of an Interpolation algorithm, since we
often ask for the value of a pixel that is not actually present in
the frame.
- Use some technique (average, median filter etc.) to choose /
calculate the most likely value of the target pixel.
- Assign this value to the target image.

#### References

**[MEAF97]** Michael Elad and Arie Feuer. Restoration of a Single Superresolution Image from Several Blurred, Noisy, and Undersampled Measured Images. *IEEE Transactions on Image Processing*, 6(12):1646—1658, 12/1997.

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