Mobile-friendly Image de-noising: Hardware Conscious Optimization for Edge Application
Srinivas Miriyala, Sowmya Vajrala, Hitesh Kumar, Sravanth Kodavanti, Vikram Rajendiran

TL;DR
This paper introduces a novel mobile-optimized image de-noising neural network designed with hardware-aware neural architecture search, achieving significant efficiency improvements while maintaining competitive accuracy on edge devices like smartphones.
Contribution
It presents the first hardware-aware NAS for a U-Net architecture tailored for mobile image de-noising, reducing parameters and latency with minimal accuracy loss.
Findings
12% fewer parameters compared to baseline models
~2-fold reduction in on-device latency on Samsung Galaxy S24 Ultra
Competitive accuracy with 18-fold reduction in GMACs compared to SOTA models
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…
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Taxonomy
TopicsImage and Signal Denoising Methods · Image Enhancement Techniques · Advanced Neural Network Applications
