Analyzing Spatially-varying Blur: Source Code
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Note: All code on this page is (as of now) released only for
non-commercial research use. Please contact
ayanc[at]eecs[dot]harvard[dot]edu if you need to use the code
for commercial purposes. If you use this code to generate any results
included in an academic publication, please cite the following
Ayan Chakrabarti, Todd Zickler, and William T. Freeman, "Analyzing Spatially-varying Blur," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
Blur Cue Code: For 1-D windows and kernels
This code implements the local blur likelihood measure described in
the paper. Given an image and a candidate kernel, it computes the
likelihood for every local window in the image being blurred by that
kernel. This implementation considers 1-D windows and kernels (at
horizontal, vertical or 45/135 degree orientations). Please see the README
and demo.m files for details.
This code implements the algorithm combining the blur cue above
with color information, to carry out the full segmentation. Includes
the blur cue code above, as well as a graph-cut implementation using
the library from Vladimir
Kolmogorov. Please see the README file for details on compiling
the graph-cut code, and demo.m for sample usage.