My study thesis (Belegarbeit) in computer vision based on the paper "Depth-Aware Motion Deblurring" of Xu and Jia.

They use spatially-varying point spread functions to deblur the image depending on the depth layer. The results can be found here: website.

conclusion: It wasn't possible to reconstruct the results from the paper. :( The estimated PSFs are too blurry. The second pass isn't possible because the disparity map ins't refined but downgraded.

motion-deblurring contains the depth-aware motion deblurring algorithm together with the a disparity estimation, region tree and edge map implementation. The top-level PSF estimation isn't working (see used two-phase kernel estimation) so there is a work-around. And the mid-level PSF estimation produces very blurry kernel.

two-phase-kernel This code isn't finished! Until now it contains the first phase of the two phase kernel estimation. This algorithm would be used for the top-level PSF estimation.

utils contains useful matlab conversion like conv2, the deconvolution with FFT and the spatial deconvolution from Levin and an implementation of the coherence shock filter from Weickert.

tools provides small programs to use same parts of the motion-deblurring algorithm like convolution, deconvolution and other to show their correctness.

external contains the source code for the match disparity algorithm from Kolmogorov which is slightly changed to fit my needs to work with OpenCV (the image data can be copied directly from OpenCV to the match algorithm).

You can build all components from the toplevel following the next steps.

• OpenCV 3.0. Installation guide

CMake is the used build tool. Use an additional build folder to have clean source folders.

# create a build directory (all CMake Files will be saved there)
mkdir build
cd build

# Create a build configuration
# CMAKE_BUILD_TYPE= Release for disabling output (doesn't show/save images ...)
cmake -D CMAKE_BUILD_TYPE=Release ..

# use make for building all make targets or specify the needed target
make

This is the main algorithm. Some tasks can be done in parallel so the number of threads can be specified.

make motion-deblurring

# Executable can be found in build/bin
# the default values can be used together with the mouse images
bin/motion-deblurring ../images/mouse-left.jpg ../images/mouse-right.jpg [--psf-width <n>] [--layers <n>] [--threads <n>] [--max-top-nodes <n>] [--max-disparity <n>] [--fft/--irls] [--help]

Work-around for missing top-level psf estimation: top-level kernels will be loaded instead of computed. So use the sample top-level kernels - place them in the folder where you starts the algorithm.

• A. N. Rajagopalan, Rama Chellappa - Motion Deblurring: Algorithms and Systems (ISBN 9781107044364)

• L. Xu, J. Jia. Depth-Aware Motion Deblurring, IEEE 2012

• L. Xu, J. Jia. Two-phase Kernel Estimation for Robust Motion Deblurring, ECCV 2010
  • technique used for top level PSF computation
  • strong edges not always good for kernel estimation
• O. Whyte, J. Sivic, A. Zisserman, and J. Ponce. Non-uniform Deblurring for Shaken Images, CVPR 2010
  • models 3D rotation of camera
  • single image and sharp + noisy image approach
• N. Joshi, S.B. Kang, L. Zitnick, and R. Szeliski. Image Deblurring with Inertial Measurement Sensors. ACM SIGGRAPH 2010
  • hardware attachment for single image deblurring
• A. Gupta, N. Joshi, L. Zitnick, M. Cohen, and B. Curless. Single Image Deblurring Using Motion Density Functions, ECCV 2010
  • using spatially invariant deconvolution methods in a local and robust way
• S. Cho and S. Lee. Fast motion deblurring. ACM Trans. Graph., 28(5), 2009
  • technique used for iterative PSF computation
  • iterative single image approach
  • novel predictive step (strong edge) and kernel estimation based on derivatives

• H. Qiu. State-of-the-Art Image Motion Deblurring Technique
  • conclusion on several single image and stereo image, blind and non-blind deblurring algorithms
• B. Kalaiyarasi, S. Kalpana. Blind Deconvolution of Camera Motioned Picture using Depth Map
  • camera shake and large depth range scene leads to non-uniform blur
  • single image with depth map
• J. Jia. Single Image Motion Deblurring Using Transparency, IEEE 2007
  • can handle camera motion blur and object motion blur
  • investigates relationship between object boundary transparency and image motion blur
• T. Kobayashi, F. Sakaue, & J. Sato. Depth and Arbitrary Motion Deblurring Using Integrated PSF, ECCV 2014
  • motion deblurring and all-in-focus imaging can be achieved simultaneously
  • motion blur caused by arbitrary multiple motions can be recovered
• Z. Hu, L. Xu, M. Yang Joint Depth Estimation and Camera Shake Removal from Single Blurry Image, CVPR2014
• H. Hirschmüller Stereo Preocessing by Semi-Global Matching and Mutual Information, IEEE 2007
  • description of SGBM-Algorithm used for disparity estimation