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A Hardware-Aware Network for Real-World Single Image Super-Resolutions
  • Rui Ma ,
  • Xian Du
Xian Du
UMass Amhers

Corresponding Author:[email protected]

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Most single image super resolution (SISR) methods are developed on synthetic low resolution (LR) and  high resolution (HR) image pairs, which are simulated by a  predetermined degradation operation, such as bicubic  downsampling. However, these methods only learn the  inverse process of the predetermined operation, which fails to super resolve the real-world LR images, whose true  formulation deviates from the predetermined operation. To  address this, we propose a novel SR framework named  hardware-aware super-resolution (HASR) network that first extracts hardware information, particularly the camera degradation information. The LR images are then super resolved by integrating the extracted information. To  evaluate the performance of HASR network, we build a dataset named Real-Micron from real-world micron-scale  patterns. The paired LR and HR images are captured by  changing the objectives and registered using a developed  registration algorithm. Transfer learning is implemented  during the training of Real-Micron dataset due to the lack  of amount of data. Experiments demonstrate that by  integrating the degradation information, our proposed  network achieves state-of-the-art performance for the blind  SR task on both synthetic and real-world datasets. Impact Statementâ\euro” The proposed HASR method has  significant impact on various areas, such as enhancing the  accurate inspection of manufactured products for quality  control and enhancing the resolution of medical images to  enable more accurate diagnosis and healthcare. Current SR solutions neglect the uniqueness of each imaging system,  hence cannot produce accurate HR images across the  different systems. Taking advantage of the known hardware information, HASR can differentiate low?resolution images across different imaging systems and  produce HR images that are closer to the real-world  scenario. Given sufficient training images, the proposed  HASR method can overcome the physical optical limitation  and generate higher quality images. The proposed method  improves the overall performance by about 0.2 dB and 0.5  dB on the synthetic and the real-world datasets,  respectively. Â