  The new trend of full-screen devices implies positioning the camera behind
the screen to bring a larger display-to-body ratio, enhance eye contact, and
provide a notch-free viewing experience on smartphones, TV or tablets. On the
other hand, the images captured by under-display cameras (UDCs) are degraded by
the screen in front of them. Deep learning methods for image restoration can
significantly reduce the degradation of captured images, providing satisfying
results for the human eyes. However, most proposed solutions are unreliable or
efficient enough to be used in real-time on mobile devices.
  In this paper, we aim to solve this image restoration problem using efficient
deep learning methods capable of processing FHD images in real-time on
commercial smartphones while providing high-quality results. We propose a
lightweight model for blind UDC Image Restoration and HDR, and we also provide
a benchmark comparing the performance and runtime of different methods on
smartphones. Our models are competitive on UDC benchmarks while using x4 less
operations than others. To the best of our knowledge, we are the first work to
approach and analyze this real-world single image restoration problem from the
efficiency and production point of view.
