DIRECTIONALLY ADAPTIVE SUPER-RESOLUTION

Date

2009-11-10

Author

Turgay, Emre
Akar, Gözde

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this paper a novel direction adaptive super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator uses gradient direction for optimal noise reduction while preserving the edges. Compared to the other edge-preserving methods, the proposed algorithm uses gradient direction in addition to the gradient amplitude for optimum regularization. The method comprises a gradient amplitude and direction estimation stage where a gradient direction map is obtained. This map guides the SR reconstruction stage through iterations. Three variations of the proposed method are compared against other edge-preserving super resolution methods. PSNR (Peak signal-to-noise-ratio), SSIM (Structural similarity index measure) values, and illustrations show that the proposed method has better performance especially on image pixel values where a strong gradient is present.

Subject Keywords

Maximum a-posteriori, Gradient, Directional regularization, Edge preservation, Super-resolution

URI

https://hdl.handle.net/11511/47764

DOI

https://doi.org/10.1109/icip.2009.5413662

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. Turgay and G. Akar, “DIRECTIONALLY ADAPTIVE SUPER-RESOLUTION,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47764.