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.
翻译:摄影机设计和成像管道的最新进展使我们得以利用智能手机捕捉高质量的图像。然而,由于智能手机相机的尺寸小和镜头有限,我们通常在所处理的图像中发现文物或退化。最常见的不愉快效应是噪音制品、碎片制品、模糊和《人类发展报告》过度暴露。图像修复的深层学习方法可以成功地清除这些文物。然而,大多数方法不适合移动设备上的实时应用,因为它们的计算和记忆要求繁重。在本文中,我们提议建立LPIENet,这是一个轻量级的感知图像增强网络,重点是在智能手机上部署。我们的实验显示,用少得多的参数和操作,我们的模型可以处理上述文物,并取得与标准基准方面的最新技术方法相比的竞争性性能。此外,为了证明我们的方法的效率和可靠性,我们直接将模型放在商业智能手机上,并评价其性能。我们的模型可以在中级商业智能手机的1秒以下处理2K分辨率图像。