1. Whiteboard Scanning using Super-resolution STATUS REPORT #2 WODE NI ADVISOR: JOHN MACCORMICK

2. Review  Preliminary result on “white board scenario”  Lack of “ground truth”  Would be better to construct a synthetic dataset  Attempt to understand the algorithm that OpenCV implementation uses  Performance issue mentioned

3. Experiment on parameters  Experiments are conducted on the following parameters of the algorithm: (1) Temporal Area Radius; (2) Iterations; (3) Kernel Size  All other parameters are kept default

4. Experiment – visual results for iterations  The output is indeed better than the original  With more iterations after 40th, there seems to be no visible improvement of quality  Why? 101 st iteration Original 1 st iteration 41 st iteration

5. Energy Minimization and Norms  In order to get closer to our high-res target, we need to minimize the energy function  Different norms can be used: L1, L2 norm  Mitzel 2009 claims that the use of L1 norm can improve the result  Which one OpenCV use? Mitzel, Dennis, et al. "Video super resolution using duality based tv-l 1 optical flow." Pattern Recognition. Springer Berlin Heidelberg, 2009. 432-441.

6. Measuring the performance  It is hard and unreliable for us to measure the quality of the output visually. Thus, a quantitative measurement is needed, as mentioned in the proposal.  Main idea: Take two images I and J, for each pixel pi and pj, compute the square of the difference. Output the sum of squares at the end.  Problem: registering images

7. Next…  Quantitative measurement of the algorithm using the synthesized data “donut”: Find the sum of square difference(SSD) between the output of the algorithm and the ground truth.  Derivation of the solution to the minimization problem: it is more complicated since Superres is an inverse problem. A regularization is needed.  Which norm is OpenCV using? If L2, do L1 and measure effect; else try some other form of regularization, e.g. “clamped” error penalty. Blurred Down-sampled

8. Thanks!