  Recent advances in camera designs and imaging pipelines allow us to capture
high-quality images using smartphones. However, due to the small size and lens
limitations of the smartphone cameras, we commonly find artifacts or
degradation in the processed images. The most common unpleasant effects are
noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep
learning methods for image restoration can successfully remove these artifacts.
However, most approaches are not suitable for real-time applications on mobile
devices due to their heavy computation and memory requirements. In this paper,
we propose LPIENet, a lightweight network for perceptual image enhancement,
with the focus on deploying it on smartphones. Our experiments show that, with
much fewer parameters and operations, our model can deal with the mentioned
artifacts and achieve competitive performance compared with state-of-the-art
methods on standard benchmarks. Moreover, to prove the efficiency and
reliability of our approach, we deployed the model directly on commercial
smartphones and evaluated its performance. Our model can process 2K resolution
images under 1 second in mid-level commercial smartphones.
