A Transform Learning Based Deconvolution Technique with Super-Resolution and Microscanning Applications

We deal with reconstruction of convolved images with known point spread functions. We adopt a feature enhanced deconvolution method. Instead of using a pre-designed sparsifying transform, we use an online transform learning based method, and reconstruct images along with a sparsifying transform. To...

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Bibliographic Details
Main Authors: Gungor, Alper, Kar, Oguzhan Fatih
Format: Conference Proceeding
Language:English
Subjects:
Computational Imaging
Convolution
Deconvolution
Image reconstruction
Image resolution
Imaging
Signal resolution
Sparsity
Super-Resolution
Transform Learning
Transforms
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Description
Summary:We deal with reconstruction of convolved images with known point spread functions. We adopt a feature enhanced deconvolution method. Instead of using a pre-designed sparsifying transform, we use an online transform learning based method, and reconstruct images along with a sparsifying transform. To avoid circular effects, we implement non-circular convolution operator using FFT based convolution and dead pixels. We use a coordinate descent type algorithm and derive the associated update steps for both circular and non-circular deconvolution. Moreover, we show single image super-resolution extension for non-circular deconvolution. We compare the proposed method to other feature enhanced deconvolution alternatives, as well as conventional methods such as Lucy-Richardson method. Finally, we demonstrate the effectiveness of the algorithm for circular deconvolution, non-circular deconvolution, and single-image super-resolution applications.
ISSN:2381-8549
DOI:10.1109/ICIP.2019.8803162