Abstract
Image enhancement is a critical task in computer vision and photography that is often entangled with noise. This renders the traditional Image Signal Processing (ISP) ineffective compared to the advances in deep learning. However, the success of such methods is increasingly associated with the ease of their deployment on edge devices, such as smartphones. This work presents a novel mobile-friendly network for image de-noising obtained with Entropy-Regularized differentiable Neural Architecture Search (NAS) on a hardware-aware search space for a U-Net architecture, which is first-of-its-kind. The designed model has 12% less parameters, with ~2-fold improvement in ondevice latency and 1.5-fold improvement in the memory footprint for a 0.7% drop in PSNR, when deployed and profiled on Samsung Galaxy S24 Ultra. Compared to the SOTA Swin-Transformer for Image Restoration, the proposed network had competitive accuracy with ~18-fold reduction in GMACs. Further, the network was tested successfully for Gaussian de-noising with 3 intensities on 4 benchmarks and real-world de-noising on 1 benchmark demonstrating its generalization ability.
Abstract (translated)
图像增强是计算机视觉和摄影中的一个关键任务，通常会受到噪声的影响。这使得传统的图像信号处理（ISP）在与深度学习的进展相比时显得不够有效。然而，此类方法的成功越来越依赖于其在边缘设备（如智能手机）上部署的简便性。本工作提出了一种新型的手机友好型网络用于图像去噪，该网络通过基于硬件感知搜索空间上的熵正则化可微神经架构搜索(NAS)获得，这是首次实现这一概念的设计。 设计的模型参数减少了12%，在三星Galaxy S24 Ultra上部署和评估时，设备端延迟提高了约两倍，内存占用减少了一半，而PSNR仅下降了0.7%。与最先进的Swin-Transformer图像恢复方法相比，该提议的网络具有竞争性的准确性，并且GMACs减少了大约18倍。 此外，在四个基准测试上成功对三个强度级别的高斯去噪进行了测试，并在一个真实世界基准测试中验证了其去噪能力，从而证明了该模型具备良好的泛化能力。
URL
https://arxiv.org/abs/2601.11684
