Advanced Machine Learning Methods for Image Deblurring

The deblurring problem is a difficult yet popular problem in the field of image processing. Blur can be caused from various reasons; movement of the photographed object, optics of the camera, image coding, etc. There are various methods for dealing with the deblurring problem; some assume prior knowledge on the way the blur occurred.  In this project, we implemented a non-blind deblurring system based on the SDM algorithm, which was proved effective in facial landmark tracking [1]. This algorithm, is composed of several regression layers each one contains two parts; feature extraction and linear regression.

The system consists of two stages; training and applying. In the training stage, features’ functions are chosen, and regression parameters are learned from a train set of blurred images, which were blurred with the same blurring kernel. In the applying stage the algorithm receives an image which was blurred in the same way as the training images, and deblurring the image using the same features’ functions and regression parameters which were learned in the training stage.

We focused on finding non-linear features in order to utilize the SDM algorithm and receive better results than the optimal linear estimator. We found that using a feature, which is based on histogram equalization, produces such results. We tested the performance of the system by comparing it to Wiener deconvolution and ForWaRD methods [][]. We found that in some cases our system produces better results than the compared methods. In General our system is of low computation complexity, and can be served as a basis for solving other problems in image processing.

Advanced Machine Learning Methods for Image Deblurring
Advanced Machine Learning Methods for Image Deblurring
Advanced Machine Learning Methods for
Image Deblurring

 

Collaboration:

Dr. Ron Rubinstein