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+# Image Reconstruction with Predictive Filter Flow
+
+
+For paper, slides and poster, please refer to our [project page](https://www.ics.uci.edu/~skong2/pff.html "predictive filter flow")
+
+
+
+<img src="https://www.ics.uci.edu/~skong2/image2/pff_icon_mediumSize.png" alt="splash figure" width="350"/>
+
+We propose a simple, interpretable framework for solving a wide range of image
+reconstruction problems such as denoising and deconvolution.  Given a
+corrupted input image, the model synthesizes a spatially varying linear filter
+which, when applied to the input image, reconstructs the desired output. The
+model parameters are learned using supervised or self-supervised training.
+We test this model on three tasks: non-uniform motion blur removal,
+lossy-compression artifact reduction and single image super resolution.  We
+demonstrate that our model substantially outperforms state-of-the-art methods
+on all these tasks and is significantly faster than optimization-based
+approaches to deconvolution.  Unlike models that directly predict output pixel
+values, the predicted filter flow is controllable and interpretable, which we
+demonstrate by visualizing the space of predicted filters for different tasks.
+
+
+**keywords**: inverse problem, spatially-variant blind deconvolution, low-level vision, non-uniform motion blur removal, compression artifact reduction, single image super-resolution, filter flow, interpretable model, per-pixel twist, self-supervised learning, image distribution learning.
+
+The jupyter script provided here is self-contained.
+Please run [task01_deblur.ipynb](https://github.com/aimerykong/predictive-filter-flow/blob/master/task01_deblur.ipynb) directly to see how our model performs for non-uniform motion blur removal.
+Please go to [this folder](https://github.com/aimerykong/predictive-filter-flow/tree/master/libs_deblur/result/._epoch-445/moderateBlurDataset_fullRes) to have a quick look at more visualizations!
+Besides, more demos are on the way.
+
+
+
+If you find anything provided here inspires you, please cite our [arxiv paper](https://arxiv.org/abs/1811.11482) ([hig-resolution draft pdf](https://www.ics.uci.edu/~skong2/slides/kf_ff_arxiv2018.pdf), 44Mb):
+
+    @inproceedings{kong2018PPF,
+      title={Image Reconstruction with Predictive Filter Flow},
+      author={Kong, Shu and Fowlkes, Charless},
+      booktitle={arxiv},
+      year={2018}
+    }
+
+
+
+![alt text](https://www.ics.uci.edu/~skong2/image2/pff_demo_motion_deblur.png "visualization")
+
+![alt text](https://www.ics.uci.edu/~skong2/image2/pff_demo_jpeg.png "visualization")
+
+![alt text](https://www.ics.uci.edu/~skong2/image2/pff_demo_SISR.png "visualization")
+
+![alt text](https://www.ics.uci.edu/~skong2/image2/pff_demo_analysisFF.png "visualization")
+
+
+last update: 10/28/2018
+
+Shu Kong
+
+
+issues/questions addressed here: 
+aimerykong At g-m-a-i-l dot com