SR²: Super-Resolution With Structure-Aware Reconstruction

Schirrmacher F, Lorch B, Stimpel B, Köhler T, Riess C (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Pages Range: 533-537

Event location: Online


DOI: 10.1109/ICIP40778.2020.9191253


Image reconstruction is particularly difficult when the type of image degradations are unknown. This may be the case if the acquisition device is unknown or the images stem from an uncontrolled environment like the internet. Yet, it may be important to reconstruct a specific piece of information from the image, such as digits from signs or vehicle license plates. Existing works incorporate such prior information with a sequential super-resolution and classification pipeline. However, this approach is prone to error propagation. In this work, we propose a new approach of connecting classification and super-resolution in parallel within a multi-task network. We show that this architecture is able to preserve structures and to remove noisy pixels although the network itself has never been trained on noisy data. We also show that this design allows to transparently trade classification and super-resolution quality. On upsampling by factor 4, we outperform sequential approaches in terms of SSIM by 10% and improve classification by 69%.

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Schirrmacher, F., Lorch, B., Stimpel, B., Köhler, T., & Riess, C. (2020). SR²: Super-Resolution With Structure-Aware Reconstruction. In Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP) (pp. 533-537). Online.


Schirrmacher, Franziska, et al. "SR²: Super-Resolution With Structure-Aware Reconstruction." Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Online 2020. 533-537.

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