A Hardware-Aware Network for Real-World Single Image Super-Resolutions

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 super-resolution (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.