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Intro

A small project to estimate the motion blur of a camera using a convolutional neural network

There are two parts to this project : - the Image Processing Part with the deconvolution algorithms - the Deep Learning part that aims to estimate the blur

The blur is for the moment linear. Currently, we only try to estimate the angle, later we'll switch to the length.

Requirements

The project is coded using Keras with a Tensorflow backend You can install the dependencies by doing: - IPython - matplotlib - sklearn - opencv (installed as cv2) - numpy - scipy

Implementation

• The linear kernel is obtained by integrating a line over one pixel so as to take into account discretization effects.
• The deconvolution is a Wiener filter. We plan to add a TV deconvolution.
• Data and training: this project has been runned on my personal computer that does not possess a GPU and has limited RAM. Therefore the training data consisted of one image linearly blurred at random angles (but constant blur intensity). This of course yields less than optimal performances on real life data but provides notheless a proof of concept.

Usage

• Testing the model on the Lena image with random rotations: run python Main.py -l 1 -p models/AngleWeights5.h5
• Visualise rotation kernel effect: go to tests and run python TestRotations
• Visualise deconvolution with a known kernel: go to tests and run python TestDeconvolution.py