Analyzing Spatially-varying Blur: Source Code
Back to Project Page
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
paper:
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.
Download
Segmentation Code:
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.
Download