Publication Type:

Conference Proceedings

Source:

IEEE International Conference on Acoustics, Speech and Signal Processing 2013 (ICASSP) , IEEE, Volume 1593, No. 1597,, Vancouver, BC, Canada, p.26-31 (2013)

URL:

https://ieeexplore.ieee.org/abstract/document/6637920

Keywords:

computational modeling, Dictionaries, Gaussian Mixture Model, Gaussian processes, GMM based method, Image denoising, image patches, Image processing, image recovery, image representation, Image restoration, multiple degraded copies, multiple observations, Noise measurement, piece-wise linear estimator, PLE, PSNR, sparse representation technique, Vectors

Abstract:

Recovery of original images from degraded and noisy observations is considered an important task in image processing. Recently, a Piece-wise Linear Estimator (PLE) was proposed for image recovery by using Gaussian Mixture Model (GMM) as a prior for image patches. Despite having much lesser computational requirements, this method yields comparable or better results when compared with the widely used sparse representation techniques for image restoration. In many situations, we might have access to multiple degraded copies of the same image, and would like to exploit the correlation among them for better image recovery. In this work, we extend the GMM based method to the multiple observations scenario, where we estimate the original image by utilizing the collective information available from all degraded copies.

Cite this Research Publication

Sandeep P. and Jacob, T., “Image restoration from multiple copies: A GMM based method”, IEEE International Conference on Acoustics, Speech and Signal Processing 2013 (ICASSP) , vol. 1593, No. 1597. IEEE, Vancouver, BC, Canada, pp. 26-31, 2013.