Abstract
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, Â 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 Â dB on the synthetic and
the real-world datasets, Â respectively. Â