Texture-Dependent Frequency Selective Reconstruction of Non-Regularly Sampled Images

Jonscher M, Seiler J, Kaup A (2016)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2016

Pages Range: 1-5

Conference Proceedings Title: Picture Coding Symposium (2016)

Event location: Nuremberg DE

ISBN: 978-1-5090-5966-9

DOI: 10.1109/PCS.2016.7906355


There exist many scenarios where pixel information is available only on a non-regular subset of pixel positions. For further processing, however, it is required to reconstruct such images on a regular grid. Besides many other algorithms, frequency selective reconstruction can be applied for this task. It performs a block-wise generation of a sparse signal model as an iterative superposition of Fourier basis functions and uses this model to replace missing or corrupted pixels in an image. In this paper, it is shown that it is not required to spend the same amount of iterations on both homogeneous and heterogeneous regions. Hence, a new texture-dependent approach for frequency selective reconstruction is introduced that distributes the number of iterations depending on the texture of the regions to be reconstructed. Compared to the original frequency selective reconstruction and depending on the number of iterations, visually noticeable gains in PSNR of up to 1.47 dB can be achieved.

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How to cite


Jonscher, M., Seiler, J., & Kaup, A. (2016). Texture-Dependent Frequency Selective Reconstruction of Non-Regularly Sampled Images. In Picture Coding Symposium (2016) (pp. 1-5). Nuremberg, DE.


Jonscher, Markus, Jürgen Seiler, and André Kaup. "Texture-Dependent Frequency Selective Reconstruction of Non-Regularly Sampled Images." Proceedings of the Picture Coding Symposium (PCS), Nuremberg 2016. 1-5.

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