SR2: Super-Resolution With Structure-Aware Reconstruction
| Autor: | Thomas Köhler, Franziska Schirrmacher, Benedikt Lorch, Bernhard Stimpel, Christian Riess |
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| Rok vydání: | 2020 |
| Předmět: |
Pixel
Noise measurement Structural similarity Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Pattern recognition 02 engineering and technology Iterative reconstruction Superresolution Pipeline (software) Image (mathematics) Upsampling 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Image resolution 021101 geological & geomatics engineering |
| Zdroj: | ICIP |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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