Machine learning deconvolution filter kernels for image restoration

Pradip Mainali; Rimmert Wittebrood

doi:10.1117/12.2077458

12 March 2015 Machine learning deconvolution filter kernels for image restoration

Pradip Mainali, Rimmert Wittebrood

Proceedings Volume 9401, Computational Imaging XIII; 940104 (2015) https://doi.org/10.1117/12.2077458
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

In this paper, we propose a novel algorithm to recover a sharp image from its corrupted form by deconvolution. The algorithm learns the deconvolution process. This is achieved by learning the deconvolution filter kernels for the set of learnt basic pixel patterns. The algorithm consists of the offline learning and online filtering stages. In the one-time offline learning stage, the algorithm learns the dictionary of various local characteristics of the pixel patch as the basic pixel patterns from a huge number of natural images in the training database. Later, the deconvolution filter coefficients for each pixel pattern is optimized by using the source and the corrupted image pairs in the training database. In the online stage, the algorithm only needs to find the nearest matching pixel pattern in the dictionary for each pixel and filter it using the filter optimized for the corresponding pixel pattern. Experimental results on natural images show that our method achieves the state-of-art result on an image deblurring. The proposed approach can be applied to recover a sharp image for applications such as camera, HD/UHD TV, document scanning systems etc.

Citation Download Citation

Pradip Mainali and Rimmert Wittebrood "Machine learning deconvolution filter kernels for image restoration", Proc. SPIE 9401, Computational Imaging XIII, 940104 (12 March 2015); https://doi.org/10.1117/12.2077458

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